JPS6386658A - Voice informing equipment - Google Patents

Abstract

PURPOSE:To automatize various kinds of informating jobs and to enhance saving time and trouble by keeping a sentence which is going to be informed as code data, automatically dialing it to plural addresses and moreover converting it into voice data to inform in terms of multiple address. CONSTITUTION:The sentence composed in a control part B is stored in a data base A as the code data and at the same time an address list which specifies a transmitted document and the transmission destination of the document is made in the control part B, then the addresses described in the address list are extracted to obtain telephone numbers corresponding to the addresses from a telephone list stored in the data base A in order to automatically dial according to the telephone numbers. Moreover the document data specified by the address list is extracted to be synthesized in speech and the voice data synthesized in speech is transmitted to telephone terminals which are automatically dialled. Thus a popular telephone terminal can be effectively utilized and various messages can be informed in voice from a work station.

Description

[Detailed Description of the Invention] [Objective of the Invention] (Field of Industrial Application) The present invention provides a voice notification device that can store text information as code data, synthesize it into speech, and automatically send it to the notification destination. Regarding.

(Prior Art) Recently, various workstations have been developed, and their processing functions are diversifying. Furthermore, information is communicated between a plurality of workstations or between a workstation and an information processing terminal via various communication lines. Therefore, so-called office automation is being promoted by utilizing the various functions of workstations.

By the way, one of the important tasks in office work is to notify various messages to related parties. For example, attendees of a preset meeting may be notified of the impending meeting and the names of materials to be brought to the meeting.

Conventionally, such meeting notifications have been carried out manually by individually calling all of the people scheduled to attend the meeting.

For this reason, the notification work requires a great deal of effort.

Therefore, efforts are being made to provide workstations with such processing functions in order to save labor.

However, not all recipients of notification own workstations or information processing terminals.

For this reason, the scope to which messages can be sent from workstations is limited.

(Problems to be Solved by the Invention) The present invention was made in view of the difficulty in individually arranging special information processing terminals that can communicate with workstations. An object of the present invention is to provide a voice notification device capable of giving voice notifications of various messages from a workstation by effectively utilizing commonly used telephone terminals.

[Structure of the Invention] (Means for Solving the Problems) As shown in FIG. 1, the present invention provides a database A that stores a telephone list containing telephone numbers corresponding to names, document data, etc. , a control unit B1 equipped with a text creation function, a destination list creation function, etc.; an acoustic synthesis unit C1 that converts code data into sound data; and a communication device equipped with an automatic dialing function. The text created by control unit B is stored in database A as code data, and at the same time, control unit B creates a destination list that specifies the transmitted document and its destination, and writes the information in this destination list. extracts the specified addressee, finds the telephone number corresponding to the addressee from the telephone list stored in database A, automatically dials according to this telephone number, and extracts the document data specified in the addressee list. Speech synthesis is performed, and the synthesized voice data is transmitted to the telephone terminal that has been automatically dialed.

If a plurality of destinations for the same text are listed in the destination list, telephone numbers corresponding to those destinations are automatically dialed in sequence.

(Operation) According to the present invention, a text to be provided for notification can be easily created and saved as code data using the text creation function of the workstation.

Then, the user specifies the document to be sent according to the created destination list, searches the telephone list in the database to find the destination telephone number, and performs automatic dialing. Then, the text indicated by the code data is converted into voice data using synthesized speech and output to the automatically dialed destination. Moreover, the document data is automatically notified by voice to all the notification destinations written in the destination list.

Therefore, it is possible to easily create a message to be notified and automatically notify it by voice by effectively utilizing a general-purpose telephone terminal.

(Example) Embodiments of the present invention will be described below with reference to the drawings.

FIG. 2 is a schematic diagram of an intelligent workstation according to an embodiment of the present invention. This intelligent workstation
It is composed of the following parts.

Bus 1: Used to transfer necessary information between each section described below.

Control unit 2: Mainly composed of a microprocessor, it controls the operations of each part of the intelligent workstation.

Image human-powered device 3: Consists of a camera, scanner, OCR, etc., and manually generates various image information.

Position coordinate human power device 4: Consists of a tablet, mouse, etc., and manually inputs specified position coordinate information.

Audio input unit 5: Consists of a microphone, etc., and inputs audio information.

Keyboard section 8: Uses multiple keys to input characters, symbols, control codes, etc.

IC card section 7: As described later, an IC card is installed therein, and necessary information is outputted between this IC card and the IC card.

Bus controller 8: Controls information transfer between each section via bus ①.

Audio output unit 9: Consists of a speaker, etc., and outputs audio information.

Display unit 10: Consists of a CRT display, liquid crystal display, etc., and displays characters, figures, images, etc.

Image output ii ⅈ Consists of FAX, color printer, etc., and prints out various image information.

Communication device 12.13. the workstation and telephone;
Or, it communicates information with other workstations, terminals, etc. installed in remote locations.

Switching device 14: Switches and uses a plurality of communication devices.

Timer part 15. Provide time information and time information to the workstation.

Encryption processing unit 16: Encrypts various information.

Voice verification unit 17. Verification processing is performed to determine whether the given voice information is a specific voice.

Image verification unit 18: Performs verification processing to determine whether the given image information is a specific image.

Speech recognition unit 19: Recognizes and processes given speech information.

Speech analysis unit 20: Analyzes and processes the voice input from the voice human power unit 5 etc. by extracting the characteristics of the voice.

Character recognition section 21. Character/symbol patterns manually input from the image input device 3 or the like are recognized and processed.

Image recognition unit 23: Recognizes graphic images etc. input from the image human power device 3 and the like.

Output format selection unit 24: Selects and controls the format of information output from the workstation.

Work environment data collection unit 25: Collects information on the functional status of the workstation and the work environment in the office.

Speech synthesis unit 26; generates synthesized speech according to the processed data.

Image composition unit 27: performs composition processing on multiple pieces of image information and executes image editing processing according to processing data.

Graphic composition processing unit 28: Composes various graphics and executes editing processing such as adding and deleting graphics according to processing data.

Audio compression/expansion unit 29: compresses and encodes audio data, and restores and expands compressed audio data.

Image compression/expansion unit 30; compresses and encodes image data, and restores and expands compressed image data.

Signal processing unit 31: Executes a series of signal processing such as encoding and compressing various signal information, decompressing and decompressing the same, and adding necessary information.

Database unit 32: Classifies various information into a plurality of relations and stores them as a database. still,
This database is constructed not only as code information but also as images, sounds, etc.

The intelligent workstation according to the present invention is basically configured with each of the above-mentioned parts, and each of the above-mentioned parts effectively utilizes its respective functions to exhibit an intelligent function as a whole. .

Next, the IC card section 7, encryption processing section 16, etc., which are not common like the aforementioned keyboard section 5, etc., but have characteristic functions in this intelligent workstation, will be explained in more detail.

First, an IC card has a semiconductor circuit such as a microprocessor and a memory circuit built into a card body 7a, which is about the size of a business card, as shown in FIG. It is constructed by providing an interface section 7b for connection to the workstation main body and a display window section 7c.

The display window section 7c is formed by embedding a transparent polarizer, and its position is set so that it does not overlap with the interface section 7b or the semiconductor circuit. Also, the card body 7a
Only the portion corresponding to the display window 7c may be transparent, or the entire substrate may be transparent.

Specifically, as shown in an exploded perspective view of FIG. 4, the IC card has a pair of cover substrates 7d.

7e, a buried substrate 7r1, a core sheet material 7g1, a printed circuit board 7h, sandwiched between these cover substrates 7d and 7e.
It is constructed by integrally bonding them together by thermocompression.

An input/output terminal 71 is provided on the printed circuit board 7h at a position facing the interface portion 7b, and a liquid crystal display device 7j is provided at a position facing the display window portion 7c. Furthermore, a semiconductor integrated circuit 7k is mounted on the printed circuit board 7h.
is provided. Further, the cover substrate 7e is provided with a metal foil gas for dissipating heat generated in the printed circuit board 7h.

Note that the holes drilled in the cover substrates 7d and 7e, the embedded substrate 7f1, and the core sheet material 7g are connected to the printed circuit board 7.
The semiconductor integrated circuits 7j and the like integrated in the semiconductor integrated circuits 7j and the like are provided at positions facing each other. The semiconductor integrated circuit 7, etc. are fitted into these holes, and the cover substrate 7d,
7e, an embedded substrate 7'', a core sheet material 7g, and a printed circuit board 7h are laminated and integrated to form an IC card.The input/output terminal 71 is exposed through a hole drilled in the cover substrate 7d, and is inserted into the workpiece. It constitutes an interface section 7b that is electrically connected to the station main body.

The liquid crystal display device 7j has a liquid crystal layer sandwiched between a pair of polyether sulfone film substrates provided with a spacer in between, as shown in FIG. , a transparent conductive film is formed on the inner surface of the film substrate, and a polarizer or a reflector is provided on the lower film substrate. If the liquid crystal display device 7j is constructed using a polyether sulfone film substrate in this way, it is easy to reduce the thickness to 0.6p or less, compared to the case where the liquid crystal display device is constructed using a glass substrate. This allows the IC card itself to be made thinner.

Further, the driving power for this IC card may be supplied from the workstation main body via the interface section 7b, or it may be built into the card. In this case, it may be incorporated as a sheet-like battery using, for example, a polymer film.

Therefore, the 4-body integrated circuit 7 has, for example, the sixth
As shown in the figure, the CPU 7p and data memory PRO
It is configured to include an M7q, an E2 PROM7r, a selection section 78 for these memories, and the like.

FROM 7Q is a large-capacity nonvolatile memory that cannot be erased or rewritten, and stores control programs for the CPU 7p, information to be permanently recorded, and the like. Also E2P
The ROM 7r is a rewritable, small-capacity nonvolatile memory, and stores information that is updated when used, such as information transaction numbers and information serial numbers.

These memories are selectively driven under the control of the selection section 7s and output information to and from the CPU 7p. The CPU 7p executes necessary information processing using these memories, and outputs information from its interface section to the intelligent workstation main body via the terminal section 71 mentioned above.

The IC card unit 7 is equipped with such an IC card and outputs information to and from the IC card.

Incidentally, it goes without saying that the IC card is not limited to the configuration described above, and it goes without saying that the IC card section 7 can be configured depending on the configuration.

Next, the encryption processing section 16 will be explained.

For example, as shown in FIG. 7, the encryption processing unit 1B includes an encryption unit 16a1, a decryption unit 16b, and a private key file unit 18c.
, a public key file section led, and a key update section LGe.

As the concept is shown in Figure 8, a given original communication text can be encrypted according to an encryption key to generate the encrypted message, or conversely, a given encrypted communication can be decrypted according to the encryption key to generate the encrypted message. Execute the process to obtain the original text.

Private file section 18c and public key file section l6d
stores the keys used for this encryption/decryption, and the key update unit L6e is in charge of updating these files.

Here, the private key is a key known only to the workstation that owns the encryption processing section 16, and is kept secret from other workstations.

On the other hand, the public key is paired with each private key set in each workstation, and is given to each other workstation and made public. The public key file section 1 [id stores the public keys published by these plurality of workstations in correspondence with each workstation.

As shown in FIG. 9, the encryption unit lea is the R3A processing unit 1.
61 and an encryption type adding unit 18j.

When attempting to encrypt the original communication text and communicate information, the original communication text is encrypted using the public key published by the workstation of the communication partner, and information indicating the type of encryption is added to the encrypted communication text. It creates communication information and communicates it. In addition, the information on the type of encryption is as follows.
For example, it includes information such as "0" indicating that it is not encrypted, "1" indicating that it is encrypted, and information indicating the encryption method.

Further, the decryption unit 16b manually receives the encrypted message encrypted and communicated by the workstation using the public key published by the self-workstation, and decrypts it using the private key corresponding to the private key. 10th
As shown in the figure, a ciphertext division section 18k, a cipher type determination section 1Bms switching section 18n.

It is configured with a 18p SRSA processing section 18q.

The ciphertext division unit tek divides the communication information communicated in the above-mentioned format into the above-mentioned cipher type information and the encrypted message, and the cipher type determination unit 18m divides the communication information from the cipher type information. Determines whether the text is encrypted or not. If the message is not encrypted, the message is output via the switching unit IGn, 16p,
If it is encrypted, the message is sent to RSA processing unit I.
Leading to Bq. The encrypted message is decrypted in the RSA processing unit 18q using the private key, and is output via the switching unit tcp.

The R5A processing unit 1B1.113Q is configured to include a block division unit 16s, a power remainder calculation unit let, and a block connection unit 16u, as shown in FIG. 11, for example.

Here, the block division section 18s divides the given signal sequence into blocks M of a constant length, and the exponentiation/remainder calculation section 181 divides each block M.

For each encryption, a signal sequence N of N=M−k(sod n) 1+ is obtained using Ik. However, n is a fixed value. This signal sequence Nl is sequentially connected and outputted via the block connection unit 16u.

In the encryption process, the signal series Mi is the original communication text, and the encrypted communication text is obtained from the original communication text as the signal series N1. In the decryption process, the signal sequence M1 is an encrypted message, and the original communication text decrypted from this encrypted message is obtained as a signal sequence N.

The key k responsible for such encryption/decryption is the aforementioned public key and private key, which are set as a pair.

Therefore, each workstation can encrypt communication information according to a public key made public by another workstation, but the encrypted communication can only be decrypted using the secret paired with the public key. Only certain workstations can know the key.

A workstation that wishes to encrypt and communicate information thus encrypts the original communication text in accordance with a public key made public by the workstation with which it communicates.

The communication information can only be decrypted by the communication partner's workstation that has the private key.

Note that all the public keys published by other workstations are stored in the public key file led (it is not necessary. For example, each workstation stores the public keys in the public key file memory provided separately for the system. The public key of each workstation is stored in a file corresponding to each workstation.When information communication becomes necessary, the public key of the communication partner is read from the public key file memory and the public key of the own workstation is opened. It may also be stored in the key file section 16.

The above is the basic configuration of the encryption processing section 16 and its functions.

Next, the image matching section 18 will be explained.

The image matching unit 18 inputs manually input image information from the image input device 3, for example, an image of an individual's face, and identifies the individual.

FIG. 12 shows a schematic configuration of this image matching section, in which 18a is an image storage section, +8b is a normalization circuit, and 18a is an image storage section;
c is a binarization (thinning) circuit, and +8d is a feature data extraction circuit. Further, 18e is a data storage section storing image data, 18r is a search circuit, 18g is a collation circuit, and 18h is an output section.

The image storage section 18a stores image information input through the image input device 3, and performs image verification processing. The normalization circuit 18b performs normalization processing on the image information stored in the image storage section 18a, and the binarization circuit 18c performs binarization processing on the image information.

Specifically, here, the normalization circuit 18b normalizes the size of the individual's face in order to identify the individual based on the image of the individual's face. The binarization circuit 18c performs, for example, edge line detection and thinning processing on the normalized face image to obtain a binary image of the image.

The feature data extraction circuit +8d performs normalization and
This method extracts feature data from binarized image information. That is, in the matching process using a face image, for example, as shown in FIG. 13, the outline of the face is extracted as one feature, and the features such as the mouth, nose, mouth, etc. in the image are further extracted. . Specifically, the contour features of the face are extracted as classified code information, and the distance between the eyes, the size of the mouth (m1), the distance between the eyes and the mouth (fin), etc. are extracted as numerical data as features of the image. ing.

The data storage unit 18c stores, for example, the 14th facial image feature data obtained for each individual in advance.
It is registered as shown in the figure. That is, the above-mentioned facial image feature data is registered for each individual using the individual's name as an identification name, and R's facial image data are connected by a pointer.

The search circuit 181' searches the data storage section 18o based on the feature data extracted by the feature data extraction circuit +8d. And the search data is collation circuit 18g
, and is compared with the feature data obtained by the feature data extraction circuit 18d.

This matching process is performed, for example, when the feature data of the input image obtained by the feature data extraction circuit 18d is set as X (i is the type of feature), and the feature data of the image registered in the data storage unit 18c is set as Y. , D-Σ IX, -Y,1, l l , and the one with the smallest value as the result of the calculation is identified as that individual. This identification result is output via the output section +8h.

The image matching unit 18 basically performs matching processing on human images in this manner, and performs, for example, individual identification of the input image.

Next, the voice recognition section 19 will be explained.

The speech recognition unit 19 is configured as shown in FIG. 15, for example. The audio input circuit 19a is for manually inputting the audio signal input from the audio input section 5 or the audio signal received by the communication device 12.13 via the public telephone line, and converts this human input audio signal into an appropriate form. Amplifiers that amplify the signal to a certain level, bandpass filters that limit the bandwidth, and A/
It is composed of a D converter and the like. The input voice is limited to a frequency band of, for example, 30 to 3400 Hz by the voice input circuit 19a, and is converted into a 12-bit digital signal at a sampling period of 12 KHz.

The acoustic processing section 19b is composed of, for example, a product-sum circuit constructed from dedicated hardware. Basically, it operates at high speed in a pipeline manner in synchronization with the audio input circuit 19a.

The acoustic processing here is performed by two types of bandpass filter groups. One of them is a 16-channel filter bank, through which changes in the spectrum of the human voice signal are extracted.

The other is a gloss filter that divides the same band into four channels, and the acoustic features of human speech are extracted through this gloss filter.

These two types of filter groups (filter bank and gross filter) are configured, for example, as fourth-order cyclic digital filters. Then, for example, the filtering output is obtained every 10m5ec. still,
Control of this sound processing section is performed using a microprogram method.

Therefore, the preprocessing/recognition unit 19c is a high-speed processor 19.
d, a pattern matching processing section 19e, a word dictionary memory 19r1, and a buffer memory 19g.

The buffer memory 19g inputs the audio signal filtered by the audio processing unit +9b, and for example, a maximum of 1
．． It stores 8 seconds worth of audio data. The high-speed processor 19d performs voice section detection, resampling, labeling, recognition processing using a transition network, and comprehensive logical judgment processing on the data stored in the buffer memory 19g. The high-speed processor L9d also performs communication with the host computer and controls the overall operation of the speech recognition section 1B.

The pattern matching processing unit 19c performs matching processing such as calculating the degree of similarity between the audio data processed by the high-speed processor 19d and the standard pattern data of the word audio registered in the word dictionary memory 19. execute,
We are looking for recognition candidates.

For example, speech words to be recognized are uttered discretely.

Therefore, the high-speed processor 19d detects a word-speech input section using input voice energy calculated every 10 Ilsec during acoustic processing, for example.

Specifically, as shown in FIG. 16, the threshold E is adaptively calculated from the background noise level and the input voice level. When the input audio signal level continuously exceeds the threshold value Eo for a certain period of time or more, the time point at which the input audio signal level exceeds the concession threshold value E is detected as the starting point S of the audio word. Thereafter, the level of the input audio signal reaches the threshold E. When the threshold value E continues to be lower than the threshold value E for a certain period of time or more. The point in time when the value falls below is detected as the end E of the spoken word.

By the way, speech recognition can be considered as a type of pattern recognition. However, there are variations in the patterns peculiar to speech, individual differences due to the gender of the speaker, the shape of the vocal organs, the method of enunciation, etc., as well as noise generated by the speaker himself or herself, noise from the surrounding environment, and even, in the case of telephone speech, public noise. There are problems with level differences and noise caused by going through a telephone line. Therefore, the problem is how to accurately and stably recognize speech by taking these into account and absorbing the above-mentioned fluctuating factors.

Therefore, the preprocessing/recognition unit 19c employs a recognition method called a hybrid structure matching method, which combines the pattern matching method and the structural analysis method in two stages.

That is, when a word speech section is detected as described above, the speech section (S, E) is first divided into 15 equal parts, and each of the 16 points is set as a resample point. Then, the spectrum at each resample point is extracted from the 16 channels of audio data (spectral time series) that have been acoustically processed as described above. Note that if there is a deviation between the sample point of the audio data and the resample point, the spectrum of the nearest point to the resample point may be extracted.

By this resampling process, 18X 1B (-256
) dimension speech pattern vector X is determined. That is, when the j-th (j -1, 2, 3, ~1B)-th sample point is , the spectrum J cottle data of 16 channels at ', is S (,) - (S , S , ~ S,)r J
lrJ, 2r3.
16r3 X-(S S -S -S
) tlrl, lr2. 2r1.
Find the vector X of the voice pattern 18r16.

However, t indicates the transposition of the matrix.

Input speech pattern vector X obtained in this way
The degree of similarity between this word and a standard pattern of word sounds registered in advance in the word dictionary memory 19f is calculated by, for example, a composite similarity method.

Here, the mark pattern of the word sounds registered in advance in the word dictionary memory 191' is (ψ ψ , ~ψ14.) 1 to' 2k (λ λ , ~λ1.k) 1 to' for the word category ωk. 2, however, it is prepared as (λlk"λ2k"~2λLk). Furthermore, ψ λ is the category, ni'
, and its fixed a value in the dispersion matrix K of the pattern vector X belonging to ωk. For such word dictionaries, the above-mentioned composite similarity is calculated. Note that in the above equation, II X II is the norm of the vector X.

Such composite similarity calculations are performed for all categories, and the top similarity value and the resulting category name are determined as a pair.

Pattern matching using such a composite similarity method enables recognition processing that eliminates many pattern variations. However, for similar patterns or patterns with 'a Fi added, the difference in similarity values between different categories may become small.

Therefore, as mentioned above, the following structural analysis method has been introduced as a supplement to the pattern matching method. This structural analysis focuses on recognition processing based on the differences in the sounds that make up word speech, and consists of a phoneme label series and an acoustic feature series.
It uses two time series.

That is, the phoneme label sequence is 1°l5l from the input audio signal.
The degree of similarity with the phoneme dictionary is calculated using the spectrum of 16 channels calculated for each e, and phonemes having a degree of similarity of −1− above a certain value are labeled and determined. Note that this phoneme label consists of six types, for example, five vowels and nasal sounds. At this time, it is preferable to prepare separate phoneme dictionaries for male voices and female voices.

Compared to vowels, which are pronounced relatively stably, it is difficult to label consonants individually as phonemes. Therefore, the consonant is labeled with its acoustic characteristics,
This is taken as characteristic information. Specifically, acoustic features are extracted from the output of a four-channel gross filter and audio energy obtained through acoustic processing. The acoustic features extracted and labeled in this way are, for example, the 17th
As shown in the figure in comparison with the characteristics of the output of the gloss filter, there are 12 types such as silence, voicelessness, friction, rupture, and energy dip.

Thus, the phoneme/acoustic label sequence obtained for the input speech is as follows over the range including the speech section (S, E):
The information is input to a transition network created for each word category, as shown in FIG. 18, for example.

The presence or absence of the specified phoneme label or acoustic feature is checked for each node of this transition network. If there is no, it will be rejected, if it is Iku, it will move to the next node,
When the feature sequence ends, the input sequence that has reached the goal of the transition network is accepted, and its category is determined. Note that the direction of sequence checking can be selected from forward to reverse for each network.

The comprehensive judgment logic is a logic that synthesizes the candidate categories ordered by pattern matching as described above and the recognition results obtained by the transition network, and makes a final judgment.

That is, in this comprehensive judgment logic, when the maximum similarity obtained by pattern matching is set to 81, this is compared with a predetermined threshold value θ. In the case of (sl×θ), this is rejected as noise.

In addition, in the case of (S1≧θ), another threshold value Δθ is used to
Categories with a degree of similarity greater than or equal to S, -Δθ) are extracted as candidates. If the number n of extracted categories is one, this is extracted as the recognition result. Furthermore, when a plurality of categories are extracted, only the categories accepted by the transition network are extracted by referring to the analysis results by the transition network. Then, the category with the greatest degree of similarity is determined as the recognition result.

In addition, if the categories extracted by threshold processing do not include those accepted by the transition network,
It is assumed that it is impossible to judge.

As described above, the pattern recognition processing result using the composite similarity method and the recognition processing result using the transition network are integrated to recognize the input word speech.

Figure 19 shows the flow of the word speech recognition processing procedure in this speech recognition unit. After speech segment detection processing, resampling processing is performed to perform pattern matching, and at the same time, labeling processing is performed to check using a transition network. After that, these recognition results are integrated to perform comprehensive judgment logic processing. Such processing is executed under a processing sequence by the high-speed processor 19d.

By the way, if you want to recognize words in continuously uttered speech instead of discretely uttered word sounds, the following may be used. That is, in this case, the input speech may be divided into various sub-intervals, and the word similarity may be determined by performing word identification for each sub-interval.

Specifically, as shown in FIG. 20, for example, all analysis frames in a human voice section are set as boundary candidates for partial sections, and the human voice section is divided into a number of partial sections. At this time, since the maximum time length D and the minimum time length D can be set for the duration of the word to be recognized, only a partial section ln within the range needs to be targeted for recognition processing.

In the example shown in FIG. 20, two partial sections are calculated assuming that the number of words in the continuously uttered voice is two. However, since the number of words in the input speech is generally unknown, it is sufficient to assume that 2 to n words exist as word candidates and to detect each partial interval. Then, calculate the word similarity for each detected subinterval,
The connections between the similarity results may be compared with each other to find the most reliable boundary of the sub-interval, and then the word recognition results for each of the sub-intervals separated by the boundary may be obtained.

However, when calculating word similarity by obtaining subintervals in this manner, the number of subintervals becomes enormous, which hinders speeding up of processing. Therefore, in practice, in consideration of speeding up the processing, for example, the number of input words is 2 to 5 words, 1
Word duration is 128-640 a+sec, 1
It is good practice to detect partial intervals with restrictions such as a word length ratio of utterances of 2.5 or less and a frame period of 16 m5ec (extracting one word out of every two in a period of 8 ssec).

In this way, it becomes possible to effectively recognize each word in continuously uttered speech.

By the way, learning of a dictionary (word dictionary) used for such speech recognition processing is performed as follows.

This learning process is roughly divided into (1) a process for finding the characteristic kernel from the vowel pattern and the consonant pattern, and (2) a process for finding the eigenvalue and solid vector for the characteristic kernel. Then, the solid HFI and the solid growth vector are measured N in order from the one with the largest eigenvalue. This process is generally called KL expansion.

First, the process of determining the characteristic kernel will be described. The characteristic kernel of an input speech pattern (learning pattern) is determined as follows, where S is the vertical vector of the learning pattern.

Here, 5-(S S -S)L II1ml'm2' own Note that this learning pattern S is given as a 64-dimensional vertical vector in the case of a consonant pattern.

In the case of a vowel pattern, it is given as a 16-dimensional vertical vector.

Therefore, the characteristic kernel is created by multiplying the vertical vector S of m learning patterns by the horizontal vector S obtained by transposing this vertical vector S! 1 is obtained by averaging each component of the matrix formed over the m learning patterns. Therefore, the number of elements of the characteristic kernel is the square of the number of elements of the vector.

Note that in order to obtain the characteristic kernel K that reflects the pattern distribution of the category through such processing, a sufficient amount of learning patterns are required. For this reason, it is necessary to store a predetermined number of learning patterns in the learning pattern memory in advance.

However, in the case of vowels, it is sufficient to use Ifl(H) to learn the learning patterns of at least 6 categories in 16 dimensions, but in the case of consonants, there are 101 categories and it is necessary to obtain the data as 64 dimensions. Therefore, it is undeniable that a huge amount of memory 82 will be required as is.

Therefore, in order to obtain a characteristic kernel K that reflects the pattern distribution using a small number of learning patterns, we perform the following process of updating the characteristic kernel, and gradually improve the characteristic kernel to reflect the pattern distribution through sequential calculations. Make it.

That is, the arithmetic process '+wSS t n is repeatedly executed on K-. However, W is a weighting coefficient when updating the characteristic kernel. This weighting coefficient W takes a positive or negative value; if it is positive, it increases the similarity of the characteristic kernel matrix to the input pattern, and if it is negative, it decreases the similarity.

Further, ′ indicates the characteristic kernel before learning the learning pattern S, and K indicates the characteristic kernel updated by learning the learning pattern S.

After that, for the characteristic kernel obtained in this way,
Processing is performed to obtain the eigenvalues and eigenvectors, and a standard pattern used in the composite similarity calculation described above is created based on the eigenvalues and eigenvectors.

The standard pattern is obtained by performing KL expansion on the characteristic kernel, for example, by performing KL expansion using power east.

Now, the characteristic kernel K has eigenvalues λ, λ2. ~λ, and the corresponding eigenvectors ξ, ξ2. ~ ■ Let us have ξ. In this case, that arbitrary vector U
are −1− eigenvectors ξ, ξ2. The linear combination of ~ξ is expressed as U Σ αi ξ1. At this time, since the relationship Kξ, −λ1 ξ, ゛ holds true, here... 〉1λ 1 ! λ 1〉1λ21〉 n LλI /λ, J > 1 (+-2, 3, ~,
n), the second term in the above equation converges to O when S becomes sufficiently large.

Therefore, the above formula Ku −αl λ1 ξl It can be made into a stone statue.

sol This shows that the ratio of (K u ), (K u ), and OO is a fixed a value λl. Also (K8
u) is shown to be proportional to the eigenvector ξl.

By the way, if the calculation based on such a theory is
In many cases, the result of the calculation will scale out immediately. Therefore, let U be any arbitrary, for example, a unit vector, and let V sol s Ll −(V )/(b ) sol
The following calculation is executed: sol 5o1(sJ, 1, 2...). Here (b)s
ol is the element with the largest absolute value of the vector (v) and so
l Yes. At this time, u = (v)/(b)s
at door+l s+1=(Ku
)/(b)s sat? =(Kv)/(b-b)s
sat s? −(K, u)/(b...
...b)o sat
Since s, from this, λ , b , ξ
.

t sat t It becomes possible to obtain us+1.

After obtaining the eigenvalue λ1 and the eigenvector ξ1 with the largest absolute value in this manner, the eigenvalue λ2 and the eigenvector ξ2 with the next largest absolute value are similarly obtained.

Here, if we consider one λI EI El for K''', then from ξILξ1= 0 ([-2, 3, ~, n), we have ξ1-λ1ξ1ξ1 ξ1-λ1 ξ for K' ξ, -.
1-λ1 ξ, -0(1-1)K' ξ - to ξ1-λ
1ξl El ξ１１ λ ・ ξ ・
(1≠1). Therefore, the above
It can be seen that has the following eigenvalues: 1λ21>...>1λ, 1>->1λ, l>0. It is assumed here that EI is normalized.

Such processing is achieved by repeatedly performing the above-mentioned processing on N' which is obtained by converting the characteristic kernel into K' - as -λ1ξ0ξ. Through this processing, eigenvalues with large absolute values and their corresponding eigenvectors are found in order, and dictionary learning is performed.

FIG. 21 shows a dictionary learning process executed based on such a calculation algorithm.

Next, the character recognition section 21 will be explained.

The character recognition unit 21 is composed of a first character recognition block that recognizes characters read by a scanner or the like, and a second character recognition block that recognizes character information manually input online via a tablet or the like. .

For example, as shown in FIG. 22, this first character recognition block includes an image memory 21a that stores image data read and input by a scanner or the like;
An area detection unit 21b detects an area in which a character to be recognized is written from the image data stored in the image memory 21a, and character data to be recognized from the image data stored in the image memory 21a according to the area detection result. The character extraction unit 21c extracts the standard pattern word 1! F21
The identification section 21e individually compares each standard character pattern of recognition target characters registered in advance in d with the character pattern extracted by the character extraction section 21e, and performs character recognition.

This character recognition block is set at a predetermined position on the FAX transmission manuscript paper 211' as shown in FIG. 23, for example, and recognizes the characters written in the character frame 21g in which the transmission destination is written. . The manuscript paper 21r on which such a transmission destination is written is used when the transmission manuscript consists of multiple sheets.
It is used as the first manuscript. Image data obtained by manually reading the first document is stored in the image memory 21a for character recognition processing.

The area detection unit 21b detects the character frame 21g from the format information of the FAX transmission manuscript paper 21r, which is determined by P.
The system obtains position information and detects the area where the character to be recognized is written. The character extraction unit 21c uses this area detection information and the projection pattern information of the image information to individually extract image data of the characters written in the character frame 21g, for example, as shown in FIG. 24. There is.

As disclosed in, for example, Japanese Patent Publication No. 12778/1983, the identification unit 21e extracts the characteristics of the character pattern from the extracted character image, and identifies the extracted character pattern and the characteristics registered in the standard pattern dictionary 21d. Pattern matching is performed with the standard pattern of each character. Then, through this pattern matching, the character category of the standard pattern that has been successfully matched is determined as the recognition result.

It goes without saying that the pattern matching method can be modified in various ways.

By the way, the second character recognition block that recognizes character information input online via a tablet or the like is configured as shown in FIG. 25, for example.

This second character recognition block includes a coordinate detection circuit 21h that sequentially detects a series of position coordinates indicating the writing stroke of a character input online via a tablet or the like.

The time series data of the position coordinates detected by the coordinate detection circuit 21h is input to the preprocessing circuit 211, and after removing small noises such as detection errors in the tablet 4,
The coordinate series storage circuit 21j sequentially stores the coordinates and subjects them to character recognition processing. Incidentally, in this preprocessing circuit 21i, for example, 1
When a number of characters are input, the size of the characters is normalized.

Further, the stroke number detection circuit 21 detects the number of written strokes, or the total number of strokes, of the character pattern from, for example, breaks in the written strokes (time-series breaks in the position coordinate data).

Accordingly, the recognition processing unit 21al selectively extracts the standard pattern with the corresponding number of strokes from among the standard patterns of the recognition target character category registered in the standard feature pattern memory 2in according to the information on the number of strokes. The characteristics of each stroke of this standard pattern and the coordinate series storage circuit 21
The stroke characteristics of the human character patterns stored in j are compared with each other (matching process). Answer decision circuit 2
1p determines the matching processing result, and obtains a recognition target character category having a stroke that corresponds to the stroke characteristics of the input character pattern as the recognition result.

In other words, the input character pattern is recognized by matching the characteristics of the stroke with the stroke characteristics of the standard character pattern according to the characteristics of the writing stroke of the character pattern input online.

Note that information on positional coordinates of end points, intersection points, break points, etc. when a written stroke is approximated by a broken line may be used as the stroke characteristics.

The character recognition unit 21 having the above-mentioned functions recognizes character information that is read manually using a scanner or the like, or character information that is manually input online via a position coordinate human-powered device such as a tablet.

Next, the figure recognition section 22 will be explained.

This figure recognition section 22 is configured as shown in FIG. 26, for example. The input unit 22a stores, for example, a captured and input graphic image, and subjects it to graphic recognition processing.

The contour tracing unit 22b divides the tracing direction of the line segment into eight directions, for example, as shown in FIG. There is. Specifically, for example, as shown in FIG. 28, a triangular figure is tracked clockwise, and information on the tracking direction is stored as, for example, rl, 2. ~2,3,4. ~4,5.7. ~
I am looking for a system of direction codes such as "7".

The segmentation unit 22c extracts singular points, such as curved parts, from the sequence of direction codes obtained in this way, and divides the outline of the figure into a plurality of characteristic parts according to the singular points.

The matching section 22d recognizes the input figure by matching the information on the figure outline segmented in this way with the characteristic information of various figures registered in the dictionary memory 22e.

For example, when the figure shown in FIG. 29 is given, from the series of direction codes obtained by contour tracing, for example, three mutually adjacent contour points (1-1), (1)
, (1+1), and perform IL smoothing processing using this as the direction code at the central contour point i. This smoothing process removes noise components.

Thereafter, the segmentation unit 22c detects end points that are characteristic points of the contour, that is, points where the curve is steep, and divides the contour around the end points.

Then, each divided contour portion is compared with the dictionary memory 22e to obtain the recognition result.

Through the above processing, as shown in FIG. 30, a round figure has no endpoints, a triangular figure has three endpoints detected, a quadrilateral figure has four endpoints, etc. Each of these figures is identified and recognized. On this occasion,
Information such as that each of the end points is convex or that the contour connecting the end points is a straight line or a curve may be used for figure identification.

On the other hand, the image recognition section 23 is configured as follows.

FIG. 31 shows a schematic configuration of this image recognition section 23, which includes an original image memory 23a, a binarization device 23b1
It is composed of a processed image memory 23c, a thinning device 23d1, and a code conversion device 23e.

The image memory 23a stores a given recognition target image, and the binarization device 23b binarizes it and stores it in the image memory 23c.

This binarization level is variably set, for example, while monitoring the binarized image on a display.

The line thinning device 23d then thins the binarized image and converts the image into a line figure.

It is something to do. The image memory 23c is rewritten with the thinned image and subjected to recognition processing.

The code conversion device 23c is configured, for example, as shown in FIG. 32, and first, a segment division unit 231' divides the "-2 thinned image into m segments. This segment division is performed, for example, by dividing the line figure at its end points, branch points, or intersections. Curvature conversion section 23
g calculates the curvature of each of the multiple segments thus divided.

Straight line/curve dividing section 23h1 curve dividing section 231. The inflection point dividing section 23j and the inflection point dividing section 23h further divide each segment divided as described above according to the information on the curvature thereof, and by these, the inflection point, the switching point between a straight line and a curved line, Points of inflection, points of radius change in the curve, etc. are detected. Through such segment division and feature point detection, information on each part constituting the image line figure is extracted.

The approximate information creation unit 23m synthesizes information on these divided segments and feature points in the segments to express the image figure, such as the position coordinates of the starting point and ending point of each segment, and the type of the segment. Obtain code information that specifies.

For example, when a human power image is given as shown in FIG. 33(a), the image line figure 23n in the input image is
is thinned and extracted, and divided into segments as shown in FIG. 3(b). In this example, an image line figure 23n in which a circle figure and a square figure are so-called skewered by straight lines is input. As shown in FIG. 33(b), the image line figure 23n of the lever is divided at the intersection, and segmented into two semicircles, two U-shaped figures, and four straight lines.

The curvature converting section 23g calculates the curvature of each divided segment as shown in FIG. The refraction point dividing section 23j and the inflection point dividing section 23h detect feature points of each segment from the curvature change points. Specifically, in the example shown in FIG. 34(a), since the curvature of the two straight lines at the bending point increases sharply, it is possible to detect the bending point from the change in the curvature. Also, Fig. 34 (b
In the example shown in ), a change in curvature is detected at the transition portion from a straight line to a curve, so the feature point can be detected from this change in curvature.

Similarly, in the example shown in FIGS. 34(C) and 34(d), it is possible to detect the feature point in the segment from the point of change in curvature.

In this manner, the image recognition unit 23 segments the given image figure and detects the feature points of each segment. Then, the image line figure is approximately expressed and recognized as code information indicating each type of a plurality of segments and their position coordinates.

Now, the voice verification section 17 is configured as follows.

This voice matching unit 17 performs individual recognition (
For example, it is configured as shown in FIG. 35.

That is, the speech given via the human voice section 17a is filtered by the phonetic filter 17b and the personal filter 17c, and the speech characteristics are extracted.

Each band of a plurality of channels of the phoneme filter 17b is set by equally dividing the audio frequency band, for example, as shown in FIG. 36(a).

Feature parameters indicating the phoneme characteristics of human speech are extracted by the phoneme filter +7b having such filter characteristics. Note that the bandwidth of each channel may be set by dividing the audio frequency band logarithmically.

On the other hand, the bandwidth of each of the plurality of channels of the personal filter 17c is set by exponentially dividing the audio frequency range, as shown in FIG. 36(b). The personal filter 17c having such filter characteristics allows more voice features from the low to middle range of the human voice to be extracted than the features in the high range. The filter output of each of these channels is obtained as a feature parameter for personal verification.

Therefore, the word recognition unit Il1 +7d
The word indicated by the input speech is recognized from the phonetic feature parameters obtained through the phonological feature parameter 7b by referring to the word dictionary 17e. The function of this word recognition is similar to that of the voice recognition section 19 described above, and the function of the sound path 1 recognition section 19 may be used as is.

Then, according to the word recognition result, a dictionary to be used for personal verification from the personal dictionary 171' is selected. This personal resignation i! f
L7r is the result of analysis by the personal filter 17e of a specific word uttered in advance by an individual targeted for speaker verification, classified and registered for each llj word.

The speaker verification unit 17g calculates the degree of similarity between each of the feature parameters of the 11 words selected from the personal dictionary 17r and the feature parameters of the input speech found in the personal dictionary 17c, and The degree values are discriminated using predetermined threshold values. Then, by comparing these discrimination results with each other, for example, the individual category with the highest similarity value and the characteristic parameter with a sufficient difference from the next highest similarity value is determined to be the speaker of the input voice. Personally identified.

Here, the characteristics of the personal filter 17c will be explained in more detail.As mentioned above, the characteristics are set to be different from those of the phoneme feature filter 17b. Considering the distinctiveness of this individuality, the distinctiveness can be evaluated, for example, by the F ratio given as F ratio - (variance between individuals)/(variance within individuals).

Now, when considering the F ratio of each channel output of the filter characteristics set in the phonetic filter 17b, an exponential trend is shown by the solid line in FIG. 37.

For this reason, in the past, individual verification has been performed exclusively using voice feature information on the high frequency side.

If it is possible to identify individuals using the R-white features in the entire frequency band rather than using only the high-frequency features of the sound, it is thought that the matching accuracy will be further improved. That is, if the value of the F ratio is 1 or more in all frequency bands and the inter-individual variance exceeds the intra-individual variance, more accurate individual matching becomes possible.

Therefore, here, as mentioned above, the personal filter 17e
Exponentially determines the characteristics of the sound, roughly extracts the characteristics of the high range where individual characteristics are noticeable, and finely extracts the sound characteristics of the low range by increasing the number of channels assigned to the low range. I try to do that.

Specifically, since the F-ratio of each channel shows an exponential trend, it is a filter bank in which the bandwidth of the high-frequency channel is increased exponentially compared to the bandwidth of the low-frequency channel. Ig configuration L, use this as personal filter 1
It is set at 7c.

According to the output of each channel of the filter 17c configured in this manner, the F ratio is as shown by the broken line in FIG. 37, and it can be seen that the F ratio in the middle range has significantly improved. As a result, it becomes possible to carry out individual verification by actively utilizing not only the voice characteristics in the high frequency range but also the voice characteristics in the middle frequency range, and it becomes possible to improve the verification accuracy.

That is, in addition to the features used for word recognition of human speech, the speech matching section 17 uses a filter bank to extract feature information that makes the individuality of the human speech noticeable. As a result, individual identification of the speaker, that is, individual verification, can be performed with high precision independently of the phoneme recognition of human speech.

Next, the speech synthesis section 2B will be explained.

The speech synthesis unit 26 includes a discriminator 28a as shown in FIG.
, a decoder 26b, a rule parameter generation device 28c, and a speech synthesizer 28d.

Determination 526a determines whether the input code string is a character string or not.
Alternatively, it is determined whether it is a code string indicating an analysis parameter for speech synthesis. This information discrimination is performed, for example, by determining the identification information added at the beginning of the input code string. When it is determined that the code string is an analysis parameter, the code string is provided to the decoder 26b, and it is decoded to obtain its phonetic parameters and prosodic parameters.

Further, when it is determined that the character string is a character string, the character string data is provided to the rule synthesis parameter generation device 26c, and is used to generate its phonetic parameters and prosody parameters.

The speech synthesizer 28d thus decodes the 426b
Alternatively, a synthesized speech wave is generated by processing the sound source wave via a vocal tract approximation filter according to the phonetic parameters and prosody parameters determined by the rule synthesis parameter generation device 28c.

To further explain the rule synthesis parameter generation device 28c, the device 28c is configured as shown in FIG. 39. The character string analysis unit 26e identifies each word in the input character string with reference to the language dictionary 26, and obtains grammatical information such as accent information, word/clause boundaries, part of speech, and conjugation for the word.

Then, phonological rules and prosodic rules are applied to the analysis results, and control information thereof is generated.

Here, the phonological rules provide information on the pronunciation of the analyzed words, realize phenomena such as rendaku and devoicing that occur due to word concatenation, and generate the phonological symbol strings.

The speech parameter generation unit 26g manually generates this phoneme symbol string, and sequentially obtains syllable parameters from the CV file 28h in accordance with the syllable unit, and interpolates and combines them. This speech parameter generation section 28g generates a phoneme parameter sequence from the phoneme symbol string.

Prosodic rules determine the boundaries of utterances and breath positions according to grammatical information such as word and clause boundaries, and determine the duration of each sound, pause length, etc.

At the same time, this prosodic rule generates a prosodic symbol string that is based on the basic accent of each word and takes into account the clause accent. The prosodic parameter generation unit 261 manually processes this prosodic symbol string to generate a prosody parameter string representing a temporal change pattern of pitch.

On the other hand, when the input code string is a code string indicating analysis parameters for speech synthesis, the decoder 28b functions as follows.

That is, when the code string of the analysis parameter indicates the cepstral coefficients of the Cv file, the code string 2B+n generally consists of the parameters P (pitch), C, C,
Bits are assigned to ~C (cepstrum coefficients) and information is compressed. Therefore, in the decoding 2S; 2f3b, the parameter conversion table 26n is used to convert and decode the information-compressed analysis parameters into a bit number matching the speech synthesizer 28d. For example, each parameter is converted to 8 bits, and a phonological parameter sequence (cepstrum coefficient) and its prosodic parameter sequence (pitch) are determined.

The speech synthesizer 26d includes a voiced sound source 28q and an unvoiced sound source (M-sequence generator) 26r, as shown in FIG. , or silent sound source wave (P-0)
are occurring selectively.

This sound source wave is input to the preamplification W2B5, subjected to signal control according to the cepstrum coefficient C of the phoneme parameter, and input to the logarithmic amplitude approximation digital filter 28t. This logarithmic amplitude approximation digital filter 26t constitutes a resonance circuit that approximates the vocal tract characteristics according to the cepstrum coefficients C, ˜C of the phoneme parameters, and filters the sound source wave. This logarithmic amplitude approximation digital filter 2
At 8t, the speech data indicated by the phonetic parameters and prosody parameters are synthesized and output.

The signal synthesized by the logarithmic amplitude approximation digital filter 28L is converted into a D/A conversion 2if! After passing through 6u, L
The signal is filtered through the P F 26v and output as a synthesized audio signal (analog signal).

In the speech synthesis section 26 configured as described above, the speech indicated by the input data series is synthesized according to the rules and output.

Next, the image composition section 27 will be explained.

As shown in FIG. 42, the image synthesis section 27 includes a control calculation section 27a, a display file memory 27b, an image synthesis circuit 27c, an image memory 27d, and, if necessary, a display 27c.

Note that this display 27c may be the display unit 10 that has been QQ'ed for the workstation.

The image synthesis circuit 27 reads parameters of vectors, polygons, and arcs written in the display file 27b under the control of a dedicated control calculation unit 27a, generates line figures indicated by the parameters, and stores them in the image memory. 27
Writing to the specified address of d. The image generation function of the image synthesis circuit 27 constructs a designated line graphic image on the image memory 27d.

This line graphic image is displayed and monitored on the display 27c under the control of the control calculation section 27a.

Further, the image generation circuit 27b has a special processing function for image generation and a filling processing function. This special processing function includes, for example, functions such as erasing hidden lines and clipping processing for overlapping yi number of image figures. The filling function consists of filling a partial area of an image figure with a specified color.

Through the functions of the image synthesis circuit 27b, various image figures are created and their synthesis processing is performed.

By the way, the synthesis of image figures generated as described above and natural pictures can be roughly divided into the following two types. One is a process of embedding and synthesizing an image obtained through arithmetic processing into a natural picture such as a landscape photograph, and the other is a process of embedding and synthesizing an image obtained by arithmetic processing into a natural picture such as a landscape photograph. It consists of the process of embedding and compositing natural images within a 1 trillion-sided image.

In the case of embedding an image in the former natural image, for example, as shown in FIG. This can be achieved by aligning this with a natural image and compositing it. Then, in the image area to which the "transparent color" code is given, the information of the natural image will be displayed as is, and in the other areas, the figure generated by the control calculation unit 27a will be displayed. As a result, image synthesis using a natural image as a background is realized. This technique is called overlay.

On the other hand, as the concept is shown in FIG. 44, a natural image may be written in the image memory, and a figure generated by the control calculation unit 27a may be written on top of it (in the front). . This method is called the two-buffer method,
It can be implemented in the comparative section 11j together with the above-mentioned overlay method.

By the way, in the latter case, in which a natural image is inserted and synthesized within a plane shown as an internal model of the control calculation unit 27a, high-speed processing is performed as follows.

The coordinate transformation required to embed a natural image on a plane into a plane facing in an arbitrary direction in three-dimensional space is given by the following equation.

CX+C5Y0C8 CX0C5Y106 However, X and Y are the coordinates on the display surface, and u and v are the coordinates on the natural image.

If this coordinate conversion process were to be executed as is, six multiplications and two divisions would be required each time one pixel is displayed, which would require an enormous amount of calculation and calculation processing time.

Therefore, here, the above-mentioned calculation is divided into two conversion processes and executed using the following intermediate coordinates (s, t). This arithmetic processing is executed at high speed using, for example, affine transformation.

u-(α S+α t+α) / t (1)
■! I+(C7s+α8 t+α9)/1S-C5X-
C4Y (2)t-CX+C5
Y0CB In other words, perspective transformation is performed using equation (1) above, and then two-dimensional affine transformation is performed using equation (2) to perform perspective transformation to an arbitrary plane at high speed. It has become.

Here, since the denominator of equation (1) is the coordinate t itself, it is easy to perform the calculation at high speed simply by slightly improving the conventionally known affine transformation circuit.

In this way, the image compositing section 27 executes various image compositing processes at high speed.

Next, the output format selection section 24 will be explained.

The output format selection section 24 is activated upon receiving a media selection request signal, and selects which medium is to be used to output data. In other words, it selects which of various media should be used to transmit information.

FIG. 45 is a schematic configuration diagram of this output form selection section 24, in which the media selection control section 24a1 manual media determination section 24
b, a partner media determination unit 24c, a media conversion table 24d, and a self-media function table 24c. Also, FIG. 46 shows this output format selection section 24.
This shows the flow of processing. The functions of the output format selection section 24 will be explained along the flow of this processing procedure.

When the media selection request signal is given, the media selection control section 24a requests the control section 2 to provide manual media information necessary for the media selection operation. Then, it issues a media information detection request and a media function identification request to the input media determining unit 24b.

The input media determining section 24b is composed of a media detecting section 24f' and a media identifying section 24g, and detects the input media given from the control section 2 in response to an information request from the media selection control section 24a, and also detects the input media given from the control section 2. It is used to identify and judge functions. For example, when the human-powered media is audio, the input media determining unit 24b identifies and determines that the function of the media is ADPCM.

Thereafter, the media selection control section 24a informs the control section 2 whether the destination of the data output is another functional block of its own terminal (within the workstation) or another function block connected via a communication line or the like. Determine whether it is a workstation or communication terminal.

When an instruction is given to output data to another workstation or communication terminal, the media selection control unit 2
4a requests the control unit 2 for identification information regarding the destination station. In response to this request, information regarding the partner station that outputs data is manually input to the t-th media determining unit 24c.

The partner media determining section 24c is a partner station identifying section 24h.

The partner station media identification unit 2412 is configured to include a function identification unit 24j, and operates upon receiving an identification information determination request from the media selection control unit 24a. Then, first, the tI hand station is identified from the identification information for the partner station, and then the media of the partner station is identified. Then, the function of the partner station's media is identified.

Specifically, for example, it is identified that the destination station to which data is to be outputted (transmitted) is an automatic FAX, its communication medium is an image, and its function is GIII type. still,
This identification of the partner station may be performed based on information sent from the partner station using its negotiation (handshake) function. In addition, if there is no negotiation function, the media detection function is transferred to the function identification unit 24.
Just let j have it. In this way, it becomes possible to identify the function according to the media information signal from the other party.

FIG. 47 shows the flow of the age-based processing procedure of the partner station. As shown in this flow, for example, it is determined whether the communication partner station is a telephone or not, and if it is a telephone, it is determined whether or not a FAX signal arrives.

If the other party's station is a telephone and a FAX signal arrives, it is sufficient to identify this as the other party's device being a fax machine. Further, if it is determined that the device is a telephone and no FAX signal arrives, it may be determined that the other party's device is a normal telephone. If it is determined that the device is not a telephone, it may be determined that the other party's device is a communication device other than a telephone.

When the media of the communication partner station is identified in this way, the media selection control unit 24a then refers to the media conversion table 24d configured as shown in FIG. 48, and selects the input media and input function. , obtain media conversion selection information corresponding to the destination device, the destination device media, and the function of the destination device.

For example, if the input media is audio, the function is ADP.
If the destination device is a GI type fax, the media of the destination device is an image, and the main media conversion function is (voice) to (code character) (code character) to (image). It is required to be. At the same time, the conversion function is (ADPCM; voice) to (GII[;FAX
) is required. At this time, dependent media conversion information exists ([If so, this is also obtained at the same time.

The media conversion information obtained in this way is sent to the control unit 2.
and selectively specifying the format of the data output.

In addition, when data output is to be performed inside the own workstation, the media selection control unit 24a refers to the own media function table 24e to find an output format in which the data can be output. According to this information, the media selection control section 24a refers to its own media conversion table of the media conversion table 24d, similarly obtains media conversion information, and provides this to the control section 2.

According to the media conversion information obtained in this way,
For example, the above-mentioned speech synthesis section 26 may be used to convert text information given as a character code series to speech information and output as data, or the speech recognition section 19 may be used to convert speech information to character code series information. The data will be output.

Next, the database section 32 will be explained.

The database unit 32 organizes and stores various data such as codes, images, and sounds, and provides this data to various application systems. Figure 49 shows this database section 3.
2, which includes an interface section 32a that executes command analysis processing, a data operation section 32b that executes database search processing, etc., and a magnetic disk device 32c and optical disk as storage media for storing various data. It is composed of a device 32d1 and its additional function section 32c.

Various data are classified and arranged into relations according to the type of data, and a database is constructed by registering each relation.

The database unit 32 will be explained below by dividing it into four parts: its logical structure, stored data, physical structure, and additional functions.

The logical structure indicates how various data are stacked when the database section 32 is viewed from the application system side. Here, data is handled as a logical structure according to the relational model, for example, as a table image as shown in FIG.

A table (relation) has several columns (attributes)
are provided, and data of a predetermined tlt is stored in each column. The 111th position (Tarapur) of data is determined as a set of values to be stored in each column.

One relation is constructed by an arbitrary number of attributes storing such Tara pulls.

However, in this model, data is stored in the database by specifying a relation name and giving values for each of its attributes. In addition, database searches can be performed by specifying attributes and attributes, and checking whether the value stored there satisfies a predetermined condition with the specified value or with a value stored in another attribute. This is done by determining whether the condition is true and extracting Tarapuls that satisfy the condition.

This search condition is given as the values being equal, unequal, smaller, larger, etc. At this time, it is also possible to specify search conditions for each of a plurality of attributes and perform logical processing (AND, OR, etc.) on the results of the condition determination. Furthermore, by specifying multiple relations and using conditions such as the value of the attribute of one relation being equal to the value of the attribute of another relation, it is also possible to perform a database search to find a predetermined Tara pull from among the number of rows of relations. be.

Furthermore, data deletion from the database is basically performed in the same manner as the above search, but instead of extracting the data, the data is deleted by deleting the data.

Furthermore, the same applies to data updating, which is performed by changing the value of a specified attribute of the obtained Tara pull and storing it.

In addition, for each relation, information (person name, person in charge code), etc. of the person who is permitted to read, add, or change data for each attribute is entered, and data protection measures are taken;
I'm being teased. Note that instead of taking this data protection measure for each attribute, it is also possible to take this measure for each relation.

It should be noted that there may be more than one person's information listed here.

In the relation example shown in FIG. 50, the data is shown as a character string, but the data stored in each relation may also be a simple bit string. In other words, the data multiplied by the relation may be not only character strings but also image information, audio information, etc.

Now, the data accumulated in this database includes the "personal schedule" relation shown in FIG. 50 mentioned above, as well as the "address book" shown in FIG. 51, for example.
“Individual work and its agents” “Operation history” “Human resources”
It consists of various posts such as ``Conference Room'', ``Conference Room Reservation'', and ``Meeting''.

As shown in this example, relations consist of those that are mainly used for personal use and those that are commonly used by many users. Personal glue rations are set up for each workstation used by each individual.
A common partition is provided at a workstation that is common to multiple users.

Note that a common workstation does not necessarily mean that its hardware is different from other workstations. It goes without saying that a personal workstation may also serve as a common workstation.

Furthermore, the number of common workstations is not limited to one, and ff1li units may be provided depending on the hierarchical level of the system. In short, a common workstation is set as one that can be easily identified from the number of workstations.

Here, the data structure of the "personal schedule" relation shown in FIG. 50 will be briefly explained.

This relation indicates that the relation name is "Personal Schedule" and that it was created by "△△△△". This relationship creator “Δ△Δ△
” allows all data operations for the relation.

Also, according to the data protection function added to this relation, everyone is allowed to read data, but only "○○○O" and "persons belonging to the engineering department" are allowed to add data. ing. Note that this "person belonging to the technical department" can be found by, for example, referring to the "personnel" relation. Also, the value of ``Data can be changed at the single person level'' is ``
Permitted only for those with 5" or higher. This "one-person level" refers to personnel relations, for example, (Director; 8) (Deputy Manager; 7) (Section Manager; 6) (Chief; 5)
) etc. to represent the position.

Furthermore, attributes such as "start time", "end time", "type", "name", and "location" are set for this relation, and data is written to each of them.

Next, a description will be given of the physical structure of the database unit 32 for actually storing the various pastes mentioned above.

The information storage unit (storage unit) stores carpenter data and can read and write any part of it at relatively high speed, and the above-mentioned magnetic disk device 32c and optical disk device 32g are inexpensive. used.

The storage of the database in this information storage unit is determined by setting the storage area of the information storage unit to a specific size, for example, several kilobytes, depending on the length of the data pull, the speed of the computer, etc.)
This is done by dividing each page into sections and managing each page as a page. As shown in FIG. 52, for example, page management information is stored in the Oth page, relation list information is stored in the first page, and information on pages in use is stored in the second page.

This list of relations indicates the location of various relations in the database.

For example, the actual data stored on the 9th and 11th pages may be stored on the 5th page or sorted based on the ration attribute (main attribute) and according to the information on the index page stored on the 10th page. It has become. This index page information indicates in which page the number of attribute values is stored.

When searching data using an attribute other than this main attribute, a second
Red and white the sub-index of page 0, and first
Obtain the sub-data shown on the page and the 22nd page. This sub-data contains only the attribute values and the above-mentioned main attribute values, and the actual data is determined using the attribute values determined here.

If the amount of actual data is enormous, such as image data or audio data, and some bit errors in the data are not a problem, these actual data may be stored on another inexpensive device such as the optical disk device 32d. The file may be stored in a suitable information storage device. In this case, the 9th page or the 11th page
The actual data page such as the page may store information to that effect and the storage location information of the actual data in the device.

However, additional functions for the database constructed in this way include, for example, automatically discarding unnecessary data. This automatic disposal of unnecessary data is performed by giving additional information to the relation, such as [disposal; allowed/disabled] and [discarding method], and by operating an erase command for each relation at predetermined intervals.

Incidentally, deletion of the table can be performed by determining, for example, whether or not the end time of conference information is before the current time. Therefore, there is no need to add any special functionality to erase such a Tara pull.

Another important additional function is data security. This data preservation function prevents data from becoming corrupted (random or lost) due to, for example, hardware failure or power outage.

Specifically, this data preservation function is realized by duplicating information, writing to magnetic tape, and the like.

In this manner, the database unit 32 classifies and organizes various data for each relation, manages the data on a page-by-page basis, and provides the data to various application systems.

Next, the work environment data collection section 25 will be explained.

The work environment data collection unit 25 collects data on past operation history for the workstation, and provides operation guidance based on this data.

Here, the work environment data collection unit 25 has a command correspondence table that associates commands corresponding to the functions of the information processing system with commands corresponding to the functions of other information systems, as shown in FIG. 53, for example. is provided.

Specifically, when the information processing system is A1 and the other information processing systems are B, C, D, etc., the commands of the other systems corresponding to the command "DELETE" in system A are "DEL", "E", etc. RA S E'
The command correspondence table indicates that the command is "RE Fvl OV E".

Figure 54 analyzes the command input by the user,
This figure shows a schematic configuration of a work environment data collection unit 25 that executes predetermined operations and various guidances.

In this work environment data collection section 25, first, a command input manually from the command human power section 25a is sent to the command analysis section 25.
b and is analyzed with reference to the command correspondence table 25c. Specifically, it is checked whether the input command is registered in the command correspondence table 25c according to the procedure flow shown in FIG. That is, when a command is input, it is first checked whether the input command is from system A or not. And the input command is system A
If the command is analyzed as a command, the command analysis unit 25
b gives the input command to the command execution unit 25d and causes it to execute a predetermined operation based on the command.

On the other hand, if the input command is not from system A, it is checked whether it corresponds to a command from another system, and if there is a command associated with it, the corresponding command is displayed on the screen display section 250. Display at. In other words, if a command used in another system (system B) is, for example, "DEL", the corresponding system A command DELETE is obtained and displayed on the double-sided display section 25e as operation guidance. I will do it.

Note that if the command corresponding to the input command does not exist in the command correspondence table 25c, the screen display section 2
A command error message is displayed in step 5e.

Specifically, the command input is processed as follows. Now, assume that a user who has experience operating systems B and C operates system A (the information processing system) for the first time. Here, if the user manually issues a command to delete the data "ABC", conventionally, the command "DELETE" for data deletion is used according to the instruction manual of System A.
You need to find a command and enter it.

However, in this case, the user can use the data erasure command 'ER' that was used in System C, for example, according to past experience.
ASE ABC" is input as shown in FIG. 56(a).

Then, the work environment data collection unit 25 analyzes this input command, obtains the system A command "DELETE" corresponding to the human command "ERACE" from the command correspondence table 25e, and displays this as a guide. As a result, even when operating System A for the first time, the user knows that the data deletion command is "DELETE" and can manually delete the data by following the guide. It becomes possible.

In addition, in order to display a list of file names,
) command “DIR” in system B as shown in
When inputting ``CATA'', the corresponding command "CATA" in the system A is similarly determined and displayed as a guide. As a result, following this guide the command “CATA
” will display a list of file names.

By utilizing the functions of the work environment data collection unit 25 in this manner, corresponding commands in the system can be guided and displayed using the manual commands used in the system with which the user has past operating experience. Therefore, the system user can operate the system by making maximum use of the knowledge acquired in the past. Then, it becomes possible to easily know the commands of the information processing system. Therefore, the user is freed from the trouble of checking the operating manual of the information processing system each time. Therefore, effects such as being able to significantly shorten the time required to learn how to operate the system can be expected.

Incidentally, when a command corresponding to an input command is obtained and displayed as a guide, the command may be executed upon receiving a judgment input of its pass/fail.

That is, Fig. 57 shows the flow of the procedure, and Fig. 58 shows an example behind the scenes, in which the erasing command "ERASE" of another system is executed, and the corresponding erasing command "DELETE" of system A is executed. When asked, decide whether this is correct or not.

When there is a positive (Y) instruction force, it is determined that the input command indicates "DELETE", and this is sent to the command execution unit 25d to execute the process.

In this way, the command correspondence is guided and at the same time the desired process is executed according to the input command and command, so there is no need to input the correct command again. In other words, the input command is automatically exchanged with the corresponding command, and the process is executed. Therefore, it is possible to further improve the operability.

Note that any number of compatible commands may exist depending on the type of system. In short, command correspondence table 25
It is sufficient to store them in association with c. It goes without saying that the command is not limited to the form of a double string of characters.

Next, the collection of system proficiency data by the work environment data collection unit 25 will be explained. Inside the work environment data collection unit 25, an external storage device and a control device are placed as hardware for executing the system proficiency data collection process.

FIG. 59 is a flowchart showing the system proficiency data collection process.

When a user enters their identification code (user number, password, etc.)
When inputted, the work environment data collection unit 25 obtains the proficiency skin chart corresponding to the identification code from the external storage device and sets it inside the device. This proficiency table stores the degree of proficiency of each user with respect to various functions of the system, and is structured as shown in FIG. 60, for example.

That is, this proficiency chart includes, for each function used, the frequency of use, the date and time of last use, the proficiency class for the function declared by the user, the proficiency class when the function was last used,
Furthermore, it is composed of information such as the degree of complexity of the function.

Here, the degree of complexity increases as the function to be used requires specialized knowledge, and as the function becomes more advanced than the basic function.

However, such a learning skin table is set up for each user,
Each is stored in an external storage device.

Note that for a user who uses the system for the first time, a learning curve for that user is created by setting a new identification code, and is registered in the external storage device.

Note that the external storage device may contain -
I: In addition to the above-mentioned proficiency table, messages for each function used corresponding to the proficiency class are registered. This message is an easy-to-understand explanation of the culms whose proficiency level is low, including the background explanation. In addition, classes with a high level of proficiency have advanced content that includes simple explanations and introductions to specialized functions.

Also, proficiency classes are, for example, A; Beginner class B: Intermediate class C; Expert class The classification is set as follows.

When the proficiency chart corresponding to the input identification code is obtained, a menu is displayed that allows the user to select the function to be used. In this menu, the user inputs, for example, a number corresponding to the function to be used.

Then, the control device determines whether the input information is an end signal or a selection signal for a function to be used, and if it is a selection signal for a function to be used, the operation is as follows.

That is, when a usage function selection signal is input, first, the proficiency table for the user is referred to, and information such as usage frequency, last usage date and time, declared proficiency class, etc. corresponding to the selected usage function is checked. Information is required.

Then, weighting is performed according to this information, and the current proficiency class is determined.

To determine this proficiency class, for example, the frequency of use is PI, the date and time of last use is T, the current date and time of use is T, the user-reported proficiency class is X, and the previous c
1 Same usage proficiency class as X2 iA, B, C1,
When the ffl miscellaneousness is P 1 and the discriminant function is F,
r F −KIP1+ 2 (To-To) 10 3G1 [
It is calculated as X1 ko 1K G [X] 5Pc. However, in the above formula, K , K .

K , K are set to appropriate values through experiments, etc., and y , y , y , z :z2. z3c, l 2 3 1 This is the evaluation weight for A, B, and C. These evaluation weights are Y kuY kuY , Z kuZ2kuZ31 2 3
! The relationship is set to an appropriate value by hand, experiment, etc.

Here, G[X] means that when X t "" A, it takes a value of Yl which is 1, and when X - B, it takes a value of Y2. Further, (T − T ”) is the number of units from the last use C8 year, month, date and time to the present converted into time.

Therefore, class determination is performed using the above-mentioned discriminant function F.

This is done as follows depending on the value of .

F<N . . . A class I N ≦F <N . . . B class r2 N2 ≦ Fr . . . C class Note that the determination thresholds N and N are appropriately determined based on experiments and the like.

When the proficiency class is determined in this way, the proficiency class corresponding to the determined proficiency class corresponds to the usage function specified as described above, but the message and error message
7. Retrieve the message from the external storage device.

Thereafter, the currently determined proficiency class is compared with the previous proficiency class stored in the proficiency table. If the proficiency level class is Kasara, a message indicating that there is a change in the proficiency level is added to the guide message etc. and written.

This proficiency class change message consists of four types of messages as shown in FIG. 62, for example. Then, it is determined according to the form of the class change and displayed together with the guide message, etc.

The user performs processing operations according to the various messages displayed in this way.

Specifically, for the function used to store created data in a file, if it is determined that the user is in the expert class (A class), a message as shown in FIG. 63 is displayed.

If the user makes a mistake in inputting information in spite of this message, an error message as shown in FIG. 64 is displayed, for example, and guidance on how to use the function is provided.

Further, if the user's proficiency level is determined to be intermediate class (B class), the jealous message shown in FIG. 65 is displayed. If the user makes a mistake in inputting information in spite of this message, an error message as shown in FIG. 66 is displayed, for example, and guidance on how to use the function is provided. Similarly, if the user's proficiency level is determined to be in the expert class (C class),
A message as shown in FIG. 67 is displayed, and if there is an error in the information input, an error message as shown in FIG. 68 is displayed to guide the operation of the function to be used.

However, when data is entered into the blank field of the guide message displayed as described above, the control device adds (+1) to the frequency of use of the proficiency table of the desired user as described above, and Update the year, month, date and time as well as the previous usage proficiency class. Then, the user is prompted to execute the function to be used, and, assuming that the function to be used has been completed, the process returns to the menu display operation for selecting the function to be used.

If the function selection signal to be used is manually input again here, the two consecutive processes will be repeated again.

However, when the termination selection signal is input manually, the proficiency table created and updated as described above is written into the proficiency file of the external storage device together with the identification code of the user concerned, and this is saved. Then, the series of processing procedures is completed.

In this way, the work environment data collection unit 25 collects data on proficiency regarding system operations and provides appropriate guidance for the operations based on the collected data.

The above is the basic configuration of this workstation and its functions.

Next, the voice notification function of this workstation will be explained.

This voice notification function consists of processing functions as shown in FIG. 69, and is realized as a part of the processing functions of the control section 2. That is, the functions of the control unit 2 used in this voice notification process are text creation means 2a, destination list creation means 2b, and storage means 2.
It consists of a C1 search means 2d, an automatic dialing means 2e, an audio text output means 2r, a search buffer 2g1, a speech synthesis buffer 2h, and a code bar 21.

Here, the text creation means 2a creates a text according to the character code and control code inputted from the keyboard section 6, displays this on the display 10, and proceeds with the creation and editing of the text in a conversational manner. The data expressed by the code string of the created sentence is then stored in the database section 32 under the control of the storage means 2C. The destination list creation means 2b is for creating a destination list for specifying documents to be notified and their notification destinations, and is interactive by inputting information from the keyboard 6 and displaying the input information on the display 10. It is designed to create a destination list. This destination list specifies which text among the texts stored in the database section 32 is to be notified and where the notification destination is.

Further, the search means 2d searches and extracts a specified sentence from the database unit 32, and also searches and extracts a corresponding telephone number from a telephone list, which is one of the database entries, according to the destination name shown in the destination list. It is something. The automatic dialing means 2C is activated based on the searched and extracted telephone number, and the communication device 12.
An automatic call (automatic dial) is made from the automatic dialing mechanism of H to the corresponding telephone terminal.

Further, the audio text output means 2r activates the speech synthesis section 26 and synthesizes the text (food string information) read from the database section 32 into speech.

The voice notification function executed using such a processing function will be explained along the processing flow shown in FIG. 70.

Generally, first, text creation processing is performed by manually inputting character codes and control codes from the keyboard 8 or the like. Similar to the processing function of a normal word processor, this text creation is performed interactively while displaying the human text data on a display, and the desired text is created and edited. Once the text is created, the data of the created text is stored in the database and the series of text creation processing is completed. still,
When the created text data is provided from another information processing device, it is stored in the database and the processing is completed.

After that, a destination list is created. For this destination list, for example, fixed form data for list creation is written into the code buffer 21 and displayed on the display. This is done by manually inputting a text name specifying the text to be notified from the keyboard 6 or the like, and information on the notification destination (destination) in association with the text name. In this case, for the same sentence name? You may manually set the UB destination. In this case, the text will be circulated according to these destinations as will be described later.

4g for voice notification is completed after the following processing procedures are completed.

When the creation of the destination list is completed, first, corresponding text data is read from the database according to the text name written therein. The text data read from this database is stored in the voice synthesis buffer 2h.

After that, the notification destination (destination) written in the destination list
information is read out. If multiple destinations are written, all of them are read and stored in the search buffer 2r. Then, the telephone number corresponding to the destination is obtained by searching a telephone list registered in advance in the database section 32, for example, as shown in FIG. 71, in which names are associated with their telephone numbers. Thereafter, the automatic dialing function is activated to automatically dial the primary telephone number, call the telephone terminal specified by the telephone number, and form a voice communication path with the notification destination.

In this case, a voice synthesis function can be used to send a message requesting confirmation of the person answering the phone, and the person who answered the phone can also be recognized from the answering line to confirm that the person responding is the person for whom the notification is intended. It's okay.

When the voice communication path is established with the notification destination in this way, the voice synthesis section 26 is then activated to sequentially synthesize the text data stored in the voice synthesis buffer 2g. Then, the synthesized voice is notified to the automatically dialed communication partner via the communication device in the form of a telephone voice.

Through the zero motion dialing process based on the destinations listed in the destination list and the voice synthesis process of the text data, the text data (message) simply created as the code data is automatically notified by voice using the telephone. It is carried out according to This notification process is performed in the search buffer 2.
This is repeated while voice notifications are being made for all of the multiple destinations stored in the . In this way, a circular notification is sent to multiple destinations.

[Effects of the Invention] As explained above, according to the present invention, text to be notified can be saved as code data, automatically dialed to multiple destinations, converted into voice data, and sent back. Can be notified. Therefore, the following effects can be achieved.

In other words, the information on the text to be notified is saved as code data, converted to audio data and output, so compared to the case where audio data is directly saved, the small memory capacity can be used more effectively and text information can be stored. can be accumulated. Furthermore, since food data can be edited more easily than voice data, it is possible to easily create and edit text to be notified.

Furthermore, since text information is notified by voice as described above, it is possible to carry out the notification process by effectively utilizing a general telephone terminal.

In other words, it is not necessary to install workstations or special information processing terminals for all the persons to whom information is to be notified for notification processing.

Therefore, for example, it is possible to automate various notification tasks in an office and save labor.

[Brief explanation of the drawing]

The figures show one embodiment of the present invention. FIG. 1 is a diagram showing a schematic configuration of a characteristic voice notification processing function of a workstation according to the present invention, FIG. 2 is a schematic configuration diagram of a workstation, and FIG. Figure 3 is an external view of the IC card attached to the workstation, Figure 4 is an exploded perspective view showing the structure of the IC card, Figure 5 is a diagram showing the structure of the printed circuit board of the IC card, and Figure 6 is the IC card. FIG. 7 is a diagram showing the configuration of the encryption processing unit in the workstation; FIG. 8 is a diagram showing the concept of encryption/decryption;
FIG. 9 is a block diagram of the encryption section, FIG. 10 is a block diagram of the decryption section, FIG. 11 is a block diagram of the RSA processing section, FIG. 12 is a diagram showing the structure of the image matching section in the workstation, Figure 13 shows an example of a face being image-processed, Figure 14 shows the structure of image data, Figure 15 shows the configuration of a single recognition unit in a workstation, and Figure 16 shows input sound. Figure 17 is a diagram showing the acoustic characteristics of consonants, Figure 18 is a diagram showing an example of a transition network, Figure 19 is a diagram showing the procedure of speech recognition processing, and Figure 20 is a diagram showing an example of the h pattern. Figure 21 is a diagram for explaining partial interval detection for input speech, Figure 21 is a diagram showing the learning processing procedure of the speech recognition dictionary, and Figure 22 is a diagram showing the configuration of the first character recognition block of the character recognition unit in the workstation. , Fig. 23 is a diagram showing an example of a FAX transmission manuscript paper on which characters to be recognized are written, Fig. 24 is a diagram for explaining the process of cutting out characters to be recognized, and Fig. 25 is a diagram showing an example of a fax transmission manuscript paper on which characters to be recognized are written. Figure 26 is a neo diagram showing the configuration of the figure recognition block in the workstation, Figures 27 to 30 are diagrams for explaining figure recognition processing, and Figure 31 is the workstation. A diagram showing the configuration of the image recognition section in the station, Figure 32 shows the +I of the code conversion unit.
′? Fig. 33 is a diagram showing an example of processing for an input image, Fig. 34 is a diagram showing feature point detection in a segment, Fig. 35 is a diagram showing the configuration of a sound matching section in a workstation, and Fig. 36 is a diagram showing an example of processing for an input image. Figure 37 is a diagram showing an example of band division of a filter bank, Figure 37 is a diagram showing filter characteristics, Figure 38 is a diagram showing the configuration of a speech synthesis section in a workstation, and Figure 39 is a configuration diagram of a rule synthesis parameter generation device. , Fig. 40 is a diagram showing the conversion structure of audio parameters, Fig. 41 is a configuration diagram of the speech synthesizer, Fig. 42 is a diagram showing the configuration of the image synthesis section in the workstation, and Figs. 43 and 44 are images. 45 is a diagram showing the configuration of the output format selection section in the workstation, FIG. 46 is a diagram showing the flow of the output format selection processing procedure, and FIG. 47 is a diagram showing the flow of the output format selection processing procedure. Figure 48 is a diagram showing the structure of the media conversion table, Figure 49 is a diagram showing the configuration of the database section in the workstation, Figure 50 is a diagram showing the data structure of the database, and Figure 51 is a diagram showing the configuration of the database section. Figure 5 showing an example of
Figure 2 shows the structure of the ration, Figure 53 shows the structure of the command correspondence table, and Figure 5 shows the structure of the command correspondence table.
Figure 4 is a diagram showing the configuration of the work environment data collection unit in the workstation, Figures 55 to 58 are diagrams for explaining the processing of the command unit, and Figure 59 is a diagram showing the flow of the system proficiency data collection process. Figure 60 is a diagram showing the structure of the proficiency level table, Figures 61 to 68 are diagrams for explaining the processing of the work environment data collection unit, and Figure 69 is a control unit that executes voice notification processing. FIG. 70 is a diagram showing the flow of voice notification processing, and FIG. 71 is a diagram showing an example of the configuration of a telephone list stored in a database used for voice notification processing. DESCRIPTION OF SYMBOLS 1... Bus, 2... Control unit, 3... Image input device, 4... Position human power device, 5... Voice input unit, 6
...Keyboard part, 7...IC card part, 8...
Bus controller, 9...Audio output device, 10...
Display unit, 11... image output device, 12.
13...Communication device, 14...Switching device, 15...
Timer part, 16... Encryption processing unit, 17... Sound verification unit, 18... Image verification unit, 19... Voice recognition unit, 20... Sound analysis unit, 21 characters Identification, Intelligence Department, 22
...Figure recognition section, 23... Image recognition section, 24.
... Output format selection unit, 25... Working environment data collection unit, 26... Sound source synthesis unit, 27... Image synthesis unit,
28... Graphic synthesis section, 29... Audio compression/expansion section, 30... Image compression/expansion section, 3I... Signal processing section, 32... Database section. Applicant's representative Patent attorney Takehiko Suzue Figure 1 Figure 4 Figure 5 Figure 6 Figure 7
Figure 10 (Sword, k) m II Ivl Figure 12 Figure 13 Figure 14 Figure 19 Figure 21 Figure 24 Figure 25 Figure 26 Figure 27 Figure 28 Figure 29 Figure 30 Figure 31 Figure 32 (a) (b) Figure 33 Figure 34 Figure 37 0H Figure 38! Figure 39 Figure 40 Figure 41 Figure 42 Figure 43 Figure 44 Figure 46 Figure 48 Figure 49 Figure 50 Figure 52 Figure 53 Figure 54 Figure 55 Figure 56 Figure 58 Figure 59 Figure 60 Figure 61 Figure 63 Figure 65 Figure 66 Figure 67 Figure 68

Claims (3)

[Claims] (1) A means for storing and storing document data, a means for creating a destination list for specifying the document to be sent and its destination, and a means for extracting the destinations written in this destination list and selecting the above from a telephone list stored in a database. means for obtaining a telephone number corresponding to the addressee; means for automatically dialing according to the telephone number retrieved from the telephone list; means for extracting document data specified in the addressee list and performing speech synthesis;
A voice notification device comprising: means for transmitting the synthesized voice data to the automatically dialed telephone terminal. (2) The voice notification device according to claim 1, wherein the document data is created according to input character code information and stored in a database. (3) The audio notification device according to claim 1, wherein automatic dialing is performed sequentially to telephone numbers corresponding to each of a plurality of recipients written in the recipient list.