JP3477751B2 - Continuous word speech recognition device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a continuous word speech recognition apparatus for recognizing a word sequence included in a speech input from the outside by using a word dictionary in which acoustic features of words to be recognized are registered.

[0002]

2. Description of the Related Art Conventionally, in this type of continuous word speech recognition apparatus, first, an input speech from the outside is analyzed at a predetermined cycle to sequentially extract acoustic feature quantities, and the acoustic feature quantity is extracted by a known DP matching method or the like. The time-series data of the extracted acoustic features is used for each data string that is the closest to the acoustic features of any word, using the acoustic features registered for each word to be recognized in the word dictionary. The word sequence of the input voice is recognized by allocating the word represented by the corresponding acoustic feature amount to each of the divided data strings.

Therefore, when the input voice is composed only of the words registered in the word dictionary in advance, the voice recognition can be satisfactorily performed, but many silent parts are included in the middle of the word of the input voice. If the input voice contains a voice (unnecessary word) that is not registered in the word dictionary, the voice recognition cannot be performed well and the word sequence may be erroneously recognized. It was

On the other hand, in order to solve such a problem, conventionally, for example, as disclosed in Japanese Patent Application Laid-Open No. 61-20095, each voice data based on voice data obtained by removing a silent portion from the voice of a word to be recognized is used. When the acoustic feature of a word is generated and registered in the word dictionary, and when recognizing the word sequence of the input voice using this word dictionary, the voice data obtained by removing the silent section from the input voice is used. It is proposed to do so.

[0005]

However, although such a measure can improve the recognition accuracy when a lot of silent sections are included in the middle of the word of the input voice, the input voice includes unnecessary words. It is not possible to prevent erroneous recognition in the case of voice recognition.
To prevent unwanted words from being input to the voice recognition device, make sure that no sound other than the voice uttered by the user is input to the voice recognition device, and that the user speaks only the words registered in the word dictionary. There was a problem that it had to be.

The present invention has been made in view of these problems, and it is possible to accurately perform voice recognition even when the input voice includes a voice (unnecessary word) other than a word to be recognized. The object is to provide a continuous word speech recognition device.

[0007]

The Means for Solving the Problems The above object was to achieve
The present invention, which has been made for the purpose of the present invention, is the contract of the present application, as illustrated in FIG.
According to the invention described in claim 1, a plurality of words to be recognized
An acoustic feature quantity is stored in advance for each word in a dictionary of words.
The memory and the input voice from the outside are analyzed at a predetermined cycle
Acoustic analysis means for sequentially extracting resonating feature quantities, and the acoustic analysis
Time-series data of acoustic features extracted sequentially by means
The divides each closest data string to the acoustic feature quantity stored in the word dictionary storing hand stage, for each data string, assigns the words representing corresponding acoustic feature quantity, the words of the input speech Speech recognition means for recognizing a sequence and the speech recognition
Output the word sequence recognized by the means to an external device.
And a continuous word voice recognition device including
The acoustic feature quantity of each word is added to the word dictionary storage means.
In addition, the acoustic feature amount of the unnecessary word that does not require voice recognition, which is obtained by averaging the acoustic feature amount of each word to be recognized, is stored as the acoustic feature amount of the word to be recognized by the voice recognition means. In addition, by the voice recognition means
From the recognized word series, it was recognized as the above unnecessary word.
The words are removed, characterized in that a unnecessary word removing means for outputting to said output means. Also, the claims of the present application
According to the invention described in 2, acoustics of a plurality of words to be recognized
Dictionary with a pre-stored dynamic feature quantity for each word
Stage and externally input voice are analyzed in a predetermined cycle
Acoustic analysis means for sequentially extracting feature quantities, and the acoustic analysis means
The time-series data of the acoustic features that were sequentially extracted in
Serial word dictionary stored most were classified for each data column approximate the hand acoustic feature amount stored in the stages, for each data string, assigns the words representing corresponding acoustic feature quantity, the word sequence of the input speech A voice recognition means for recognizing, and a voice recognition means
Output means for outputting a more recognized word sequence to an external device
And a continuous word speech recognition device comprising:
In addition to the acoustic features of each word in the dictionary storage means,
The acoustic feature quantity of unnecessary words which do not require speech recognition obtained by averaging the acoustic feature quantity of all a word to be the recognition,
It is stored as an acoustic feature amount of a word to be recognized by the voice recognition means, and further recognized by the voice recognition means.
It is characterized in that an unnecessary word removing means for removing the word recognized as the unnecessary word from the generated word series and outputting it to the output means is provided.

[0008]

As described above, in the continuous word speech recognition apparatus of the present invention, the word dictionary storage means stores acoustic features of a plurality of words to be recognized and acoustic features of unnecessary words that do not require speech recognition. Is stored as the acoustic feature amount of the word to be recognized by the voice recognition means.

When a voice is input from the outside, first, the acoustic analysis means sequentially analyzes the input voice in a predetermined cycle to sequentially extract acoustic feature quantities. Then, the voice recognition means uses the time-series data of the acoustic feature quantities sequentially extracted by the acoustic analysis means for each data string that is the closest to the acoustic feature quantities of the words and unnecessary words stored in the word dictionary storage means. By dividing each of the divided data strings into a word or an unnecessary word represented by the corresponding acoustic feature amount,
Recognize word sequences in input speech.

When the voice recognition means recognizes the word sequence in the input voice in this way, the unnecessary word removing means removes the word recognized as the unnecessary word from the word sequence and outputs it to the output means. . As a result, the words to be recognized included in the input voice are continuously output in time series from the output means, and the external device is provided with only the necessary word sequence input by the user's voice. Will be done.

That is, according to the present invention, not only the acoustic feature amount of a word to be recognized but also the acoustic feature amount of an unnecessary word that does not need to be voice-recognized is registered as a word dictionary, and the voice recognition means is registered. By recognizing the unnecessary words included in the input voice as one word, it is possible to prevent the unnecessary word area in the input voice from being erroneously recognized as one of the words to be recognized. .

As the word dictionary of unnecessary words, it is only necessary to register acoustic feature quantities capable of recognizing voices other than words to be recognized, and the number may be one or plural. as described in 1, to set a material obtained by averaging the acoustic feature quantity of each word to be recognized, Wakashi Ku is as claimed in claim 2, acoustic feature quantity of all words to be recognized If an averaged value is set, unnecessary words can be satisfactorily recognized by one dictionary. This is because the average feature amount obtained by averaging the acoustic feature amounts of the words to be recognized has a large variance, and the unnecessary word portion in the input voice is more Because it will be close.

[0013]

Embodiments of the present invention will be described below with reference to the drawings. First, FIG. 2 is a block diagram showing the overall configuration of a continuous word voice recognition device (hereinafter, simply referred to as a voice recognition device) of an embodiment to which the present invention is applied. Note that FIG. 2 is a block diagram showing a functional configuration of the voice recognition device, and does not show a hardware configuration.

As shown in FIG. 2, the speech recognition apparatus of this embodiment has a dictionary creating section 2 for creating a word dictionary 10 used for speech recognition, and a word dictionary 10 created by the dictionary creating section 2.
And a recognition unit 4 for recognizing a word sequence in a voice input from outside using.

Here, the dictionary creating section 2 uses a CPU and RO.
To create a word dictionary 10 required for voice recognition by processing of a microcomputer including M and RAM, and to store the created word dictionary 10 in a predetermined storage area of a storage element such as a RAM or an IC card. The required word voice data 6 collected for each word (hereinafter referred to as a necessary word) to be voice-recognized is used to obtain the acoustic feature amount of each necessary word used for voice recognition. Is used as a dictionary for recognizing necessary words, and the necessary word voice data 6 is also used to obtain acoustic feature amounts of unnecessary words that do not require voice recognition. And an unnecessary word dictionary creating unit 14 that is set as the dictionary of.

The recognition unit 4 also includes a microphone for converting ambient voice into digital data and taking in the digital data, A /
A voice recognition unit 24 that recognizes a word sequence in the input voice from a voice input unit 22 including a D converter, input data from the voice input unit 22, and the word dictionary 10, and a recognition result by the voice recognition unit 24. Is output to an external display device or an external device that operates according to the recognition result. The voice recognition unit 24 is realized by the processing of the microcomputer, like the necessary word dictionary creating unit 12 and the unnecessary word dictionary creating unit 14.

Next, the operations of the necessary word dictionary creating section 12, the unnecessary word dictionary creating section 14, and the voice recognition section 24 will be described with reference to the flow charts shown in FIGS. First, Fig. 3
9 is a flowchart showing a necessary word dictionary creating process executed in the necessary word dictionary creating unit 12.

As shown in FIG. 3, when the necessary word dictionary creating process is started, all (N) necessary words included in the necessary word voice data 6 are sequentially registered in the word dictionary 10 one by one. First, in step 100, an initialization process of setting an initial value "1" in a counter n that counts the number of necessary words is executed.

Then, in the subsequent step 110, the necessary word (n) corresponding to the value of the counter n from the necessary word voice data 6
Of the audio data, and in the next step 120, the read audio data is read in a frame cycle (for example, 20 mse).
The acoustic analysis is performed for each c.) and the acoustic feature amount (for example, cepstrum) is extracted.

The reading of voice data in step 110 is executed by reading all m voice data when there are m voice data forming the necessary word (n). In addition, when there are m pieces of voice data forming the necessary word (n) in this way, in step 120, m
The individual acoustic feature amount will be obtained.

Next, in step 130, the above step 1
The m acoustic feature amounts obtained in step 20 are averaged to obtain one average feature amount that is the acoustic feature amount of the entire required word (n), and in step 140, this average feature amount is used as the required word amount. It is written in the required word (n) item of the word dictionary 10 as the acoustic feature amount of (n).

Then, in the following step 150, it is judged whether or not the value of the counter n has reached the number N of necessary words to be registered, so that all (N) necessary words included in the necessary word voice data 6 are necessary. It is determined whether or not the dictionary has been created for the word. If the dictionary has not been created for all N required words, the process is executed in step 160.
The value of the counter n is incremented in order to shift to the creation of the dictionary of the next required word in the required word voice data 6, and the process proceeds to step 110 again. If it is determined in step 150 that the dictionary has been created for all N necessary words, the process ends.

As described above, in the necessary word dictionary creating process, the average feature amount is calculated for each necessary word in the necessary word voice data 6 and registered in the word dictionary 10. Therefore, N dictionary items corresponding to the number of necessary words are set in the word dictionary 10, and the acoustic feature quantity representing the entire necessary word is stored in the dictionary item of each necessary word.

Next, FIG. 4 is a flowchart showing the unnecessary word dictionary creating process executed by the unnecessary word dictionary creating section 14. As shown in FIG. 4, when the unnecessary word dictionary creating process is started, all the words included in the necessary word voice data 6 (N
Step 2 to perform acoustic analysis of necessary words
At 00, an initialization process of setting an initial value "1" in a counter n that counts the number of necessary words is executed.

Then, in the following step 210, the necessary word (n) corresponding to the value of the counter n from the necessary word voice data 6
Of the voice data, and in the next step 220, the read voice data is read in a frame cycle (for example, 20 mse).
The acoustic analysis is performed for each c.) and the acoustic feature amount (for example, cepstrum) is extracted. Note that the processing of steps 210 and 220 is the same as step 11 of the necessary word dictionary creation processing.
0 and step 120.

When the acoustic analysis of the necessary word (n) is completed in this way, it is judged in step 230 whether or not the value of the counter n is the number N of the necessary words, and the necessary word voice data is obtained. It is determined whether or not the acoustic analysis is completed for all (N) necessary words included in No. 6. If the acoustic analysis has not been completed for all N required words, the process proceeds to step 240, and the value of the counter n is incremented in order to perform the acoustic analysis for the next required word in the required word voice data 6. And step 210 again
Move to.

On the other hand, when it is determined in step 230 that the acoustic analysis has been completed for all N necessary words, the process proceeds to step 250 this time, and is determined by repeatedly executing step 220. By averaging the acoustic feature amounts of all (N) required words, one average feature amount obtained by averaging all necessary words is obtained. Then, in the following step 260, this average feature amount is written in the unnecessary word item of the word dictionary 10 as the acoustic feature amount of the unnecessary word,
The process ends.

As described above, in the unnecessary word dictionary creating process, the average feature amount of all necessary words is registered in the word dictionary 10 as the acoustic feature amount of the unnecessary word. Therefore, the word dictionary 10 has “N + 1” dictionary items in which 1 is added to the number N of necessary words. Note that the average feature amount of all necessary words is set as the acoustic feature amount of unnecessary words because the average feature amount of all necessary words has a large variance, so the unnecessary word portion in the input speech is This is because it is closer to the average feature amount of all necessary words than the feature amount of That is,
In this embodiment, since the average feature amount of all the necessary words is set as the acoustic feature amount of the unnecessary words, the unnecessary words in the input voice can be accurately recognized by one unnecessary word dictionary. is there.

Next, FIG. 5 is a flow chart showing a voice recognition process executed in the voice recognition unit 24. As shown in FIG. 5, when the voice recognition process is started, first, in step 300, the voice data input from the voice input unit 22 is processed in a predetermined frame cycle (for example, 20 msec.).
A process as an acoustic analysis means for sequentially performing acoustic analysis for each to extract an acoustic feature amount (for example, a cepstrum) is executed, and the number of acoustic feature amounts obtained for each frame by this acoustic analysis is calculated as a frame length F. Memorize as.

As described above, when the time-series data of the acoustic feature amount obtained by acoustically analyzing the input voice in the predetermined frame period is obtained in step 300, the known DP matching method is used in steps 310 to 430. Then, the time-series data is divided into some sections, and a process as a voice recognition means for determining which word stored in the word dictionary 10 corresponds to each section is executed.

That is, the time-series data in which the acoustic feature amount of the input voice is stored for each frame is divided into all sections that can be divided in frame units, and for each section, the data (acoustic (Feature amount) and the acoustic feature amounts of necessary words and unnecessary words registered in the word dictionary 10 are calculated (distance), and all frames are divided into sections where the distance is the minimum. The word sequence in the input speech is obtained by assigning the word with the smallest distance to.

Hereinafter, this process will be described step by step. As shown in FIG. 5, first in step 310, an initialization process of setting initial values “0” to variables T (1), S (1), W (1) used in the subsequent processes is executed. Then, in subsequent steps 320 and 330, the initial value "1" is set to the end point frame j and the start point frame i, which represent the region for which the distance to the word dictionary 10 is to be calculated, from the time-series data of the acoustic feature quantity. In step 340, the initial value "1" is set to the item number n of the word in the word dictionary 10 used for calculating the distance.

Next, in the following step 350, from the time-series data obtained in the above step 300, from the starting point frame i initialized in steps 320 and 330 or updated in the subsequent processing, to the end point i. The acoustic feature up to the frame j is read, the acoustic feature of the word of the item number n is read from the word dictionary 10, and the distance Dij (n) representing the degree of coincidence of each feature is calculated from the start frame to the start frame. T (i), which is the distance calculated up to i
Is calculated as an initial value.

Then, in the following step 360, it is judged whether or not the item number n of the word dictionary 10 matches the number "N + 1" of words registered in the word dictionary 10, so that from the i frame to the j frame. For acoustic features, all words (that is, N
It is determined whether or not the distances between the necessary words and the unnecessary words) have been calculated, and it is determined in step 360 that the distances to all the words registered in the word dictionary 10 have not been completed. If it is determined, the process proceeds to step 370, and the value of the item number n is incremented in order to calculate the distance to the word of the next item number (n + 1) in step 350, and the process returns to step 350. Transition.

On the other hand, in step 360, the word dictionary 1 is selected for the acoustic feature amount from the i-th frame to the j-th frame.
When it is determined that the calculation of the distances to all the words registered in 0 is completed, the process proceeds to step 380, and it is determined whether or not the starting point frame i matches the ending point frame j. It is determined whether or not the distance is calculated by shifting the frame i to the end point frame j by one frame. Then, in this step 380, the starting point frame i
If it is determined that does not match the end point frame j, the process proceeds to step 390 to shift the start point frame i to the next frame (i + 1) and calculate the distance.
The value of the starting point frame i is incremented, and the process proceeds to step 340 again.

As a result, in the area where the starting point frame i is shifted by one frame, the item number 1 registered in the word dictionary 10
Word from item number “N + 1” (that is, unnecessary word)
The distances for all the words up to are calculated again, and by repeating this process, all regions that can be divided in frame units in the time series data from the first frame to the end frame j of the time series data. The distance between the acoustic feature amount of the input voice in and the combination of all the words registered in the word dictionary 10 is calculated.

Next, when it is determined in step 380 that the starting point frame i matches the ending point frame j, in the following step 400, the above step 350 is performed for the currently set ending point frame j. The distance "minDij (n)" that minimizes the value is selected from the distances Dij (n) repeatedly calculated in step 1, and the value of the distance is set to the variable T (j), and the start point frame corresponding to the distance is selected. The value of i is stored as a variable S (j), and the dictionary item n of the word dictionary 10 corresponding to the distance is stored as a variable W (j).

Then, in the following step 410, it is determined whether or not the value of the end point frame j matches the value of the frame length F, so that the end point frame j is changed from the initial value "1" to the end frame F of the time series data. It is determined by shifting one frame at a time whether or not the processes in steps 330 to 400 have been executed, and in step 410, it is determined that the value of the end frame j does not match the value of the frame length F. , Go to step 420 and set the end frame j to 1
The value of the end frame j is incremented in order to shift the frames and execute the processing in steps 330 to 400,
The process moves to step 330 again.

As a result, the acoustic feature amount of the input voice and the word dictionary in all the regions that can be divided frame by frame in the time-series data from the start frame to the end frame j in which the end frame j is shifted by one frame. The distances to all combinations of words registered in 10 are calculated, and the section with the minimum distance is obtained. By repeating this process, in step 400, from the end point frame “1”, Array of variables T (1), T (2), ... T (F), S in each area up to the end frame “F”
(1), S (2), ... S (F), and W (1), W
(2), ... W (F) will be set.

Then, in step 400, the variable T
When (F), S (F), and W (F) are obtained, the value of the end point frame j corresponds to the value of the frame length F, so an affirmative decision is made in step 410, and the processing is executed. Moves to step 430. In step 430, the variable S (j) and the variable W sequentially obtained in step 400 are obtained.
By traversing (j) and j = F from the end frame side, the word sequence having the minimum sum of distances to the words registered in the word dictionary 10 is obtained.

That is, the variable S (j) is a combination that minimizes the total sum of the distances to the words registered in the word dictionary 10 in the area from the 1st frame to the jth frame of the time-series data of the input voice. , The variable W (j) represents the word in that interval, so W
In (F), the word corresponding to the last section is stored, and S
The starting point frame of the last section is stored in (F). Therefore, by tracing back the variable S (j) and the variable W (j) sequentially obtained in step 400 from the end frame side of j = F, the word corresponding to the variable W (F) is determined as the final word. If you look at the variable W (j) that is set as a word and has the end frame j that is one frame before the start frame represented by the variable S (F), you can see the penultimate word. The word sequence of the input speech is set by the procedure of setting the word as the word of the second section, and further finding and setting the word one before the variable S (j) that represents the starting point frame of the second-to-last word. You can easily ask for it.

Thus, when the word sequence of the input voice is obtained in step 430, the process proceeds to step 440,
A process as an unnecessary word removing means for removing a word recognized as an unnecessary word from this word series is executed. In other words, one unnecessary word is registered in the word dictionary 10 in addition to the N necessary words, and in the time-series data of the input voice, a section that is close to the acoustic feature amount of this unnecessary word is regarded as an unnecessary word. Since the word series is recognized and the word series is set, the word series that includes only the necessary words is generated by removing the words that are recognized as unnecessary words from the word series. As a result, the recognition result output unit 26 outputs data representing a word series consisting of only the necessary words.

As described above, in the speech recognition apparatus of this embodiment, the word dictionary 10 averages the acoustic feature amounts of all the necessary words together with the acoustic feature amounts of the necessary words which are the words to be recognized. By registering the converted average feature amount as the acoustic feature amount of the unnecessary word, the region of the unnecessary word in the input voice is recognized as the unnecessary word by using the registered unnecessary word dictionary during voice recognition. In addition, after the voice recognition is completed, the words recognized as unnecessary words are removed from the recognition result, so that a word sequence including only the necessary words is output.

Therefore, when the input voice contains an unnecessary word, the area of the unnecessary word is erroneously recognized as one of the necessary words registered in the word dictionary as in the conventional case. It is possible to improve the recognition accuracy of the word series without causing the problem and to output the accurate word series issued by the user to the external device.

Although the voice recognition apparatus of this embodiment is constructed so that the voice recognition is performed by the DP matching method, the voice recognition may be performed using, for example, an HMM (Hidden Markov Model). In this case, when the word dictionary is created, step 130 in FIGS. 3 and 4 is performed.
In addition, instead of obtaining the average feature amount of the acoustic feature amount (for example, cepstrum) obtained by performing the acoustic analysis for each frame period when obtaining the acoustic feature amount of the necessary word and the unnecessary word in step 250, the Forward- The parameters of the HMM are estimated by using the Backward algorithm or the like, and the obtained parameters of the HMM are registered in the word dictionary 10 as the acoustic feature quantities of the necessary word and the unnecessary word in steps 140 and 260. In the case of speech recognition, in step 350 in FIG.
Instead of calculating the distance Dij (n) between the time-series data of the acoustic feature amount from the end point frame j to the acoustic feature amount of the word dictionary item n, based on the model having the parameter of the word dictionary item n, Start frame i to end frame j
Up to the maximum likelihood instead of calculating the likelihood of the time-series data of the acoustic features up to and calculating the starting point frame i and the dictionary item n of the section where the distance Dij (n) is the minimum in step 400. The starting point frame i and the dictionary item n of the section may be obtained.

[0046]

As described above, in the continuous word speech recognition apparatus of the present invention, not only the acoustic feature amount of the word to be recognized but also the acoustics of unnecessary words that need not be recognized as a word are used as a word dictionary. The feature amount is also registered, and the unnecessary words included in the input voice are recognized as one word at the time of voice recognition, and after the voice recognition is completed, the word recognized as the unnecessary word is removed from the recognition result. Thus, a word series consisting of only the necessary words is output.

Therefore, according to the present invention, when the input speech includes an unnecessary word, the unnecessary word may be erroneously recognized as a word to be recognized, as in the conventional case. Therefore, the recognition accuracy of the word series can be improved, and the accurate word series issued by the user can be output to the external device.

[Brief description of drawings]

FIG. 1 is a block diagram illustrating a configuration of the present invention.

FIG. 2 is a block diagram showing a configuration of a voice recognition device according to an embodiment.

FIG. 3 is a flowchart showing a necessary word dictionary creating process executed by a necessary word dictionary creating unit according to the embodiment.

FIG. 4 is a flowchart showing an unnecessary word dictionary creating process executed by an unnecessary word dictionary creating unit of the embodiment.

FIG. 5 is a flowchart showing a voice recognition process executed in the voice recognition unit of the embodiment.

[Explanation of symbols]

2 ... Dictionary creating unit 4 ... Recognition unit 6 ... Necessary word voice data 10 ... Word dictionary 12 ... Necessary word dictionary creating unit 14 ...
Unnecessary word dictionary creation unit 22 ... Voice input unit 24 ... Voice recognition unit 26 ... Recognition result output unit

Continuation of front page (56) Reference JP-A-5-313690 (JP, A) JP-A-61-20095 (JP, A) JP-A-59-119400 (JP, A) JP-A-61-52698 (JP , A) JP 62-65092 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) G10L 15/10 G10L 15/20

Claims (2)

(57) [Claims] 1. An acoustic feature quantity of a plurality of words to be recognized is stored in advance for each word, and a word dictionary storage means is used to analyze an input voice from the outside in a predetermined cycle to sequentially determine the acoustic feature quantity. The acoustic analysis means for extraction and the time-series data of the acoustic feature quantity sequentially extracted by the acoustic analysis means are divided into data strings that are closest to the acoustic feature quantity stored in the word dictionary storage means. , For each data string, assign the word represented by the corresponding acoustic feature,
A continuous word voice recognition device comprising: a voice recognition unit that recognizes the word sequence of the input voice; and an output unit that outputs the word sequence recognized by the voice recognition unit to an external device. In addition to the acoustic feature amount of each word, the acoustic feature amount of an unnecessary word that does not require voice recognition is obtained by averaging the acoustic feature amount of each word to be recognized by the voice recognition means. It is stored as an acoustic feature amount of a word to be recognized, and further, the word recognized as the unnecessary word is removed from the word sequence recognized by the voice recognition means, and the word is output to the output means. A continuous word voice recognition device characterized by comprising unnecessary word removing means. 2. Acoustic features of a plurality of words to be recognized
, A word dictionary storage means stored in advance for each word , and an acoustic feature quantity by analyzing an input voice from the outside in a predetermined cycle.
And a time series of acoustic feature quantities sequentially extracted by the acoustic analysis means.
The column data is divided into data strings that are closest to the acoustic features stored in the word dictionary storage means, and the words represented by the corresponding acoustic features are assigned to each data sequence.
A voice recognition means for recognizing the word sequence of the input voice, and the word sequence recognized by the voice recognition means to an external device.
In a continuous word speech recognition device comprising: output means for outputting , in the word dictionary storage means, the acoustic feature quantity of each word is stored.
In addition, the acoustic feature quantity of unnecessary words which do not require speech recognition obtained by averaging the acoustic feature quantity of all a word to be the recognition, acoustic features of a word to be recognized by the speech recognition means It is stored as a quantity, and further in the word sequence recognized by the voice recognition means.
The continuous word voice recognition device is characterized in that an unnecessary word removing means for removing the word recognized as the unnecessary word and outputting it to the output means is provided.