JP2005118371A - Game machine - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To control a game so that various events are provided according to the number of utterance information uttered by a user. <P>SOLUTION: A conversation processing part comprises a conversation database means (a conversation database) storing a plurality of topic specifying information, a conversation control means (a conversation control part) for storing the history of conversation determined by previous conversations and previous sentences of reply and selecting a sentence of reply by collating the topic specifying information determined by the history of conversation and the utterance information, and a counting means (an utterance information number counting means) for counting the number of utterance information received in a prescribed period of time. The game processing part executes an event correlated to the number of the utterance information based on the number of the utterance information counted by the counting means. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a game machine, and more particularly, to a game machine capable of executing control of events in a game based on how much user's utterance has occurred within a predetermined period.

  With the recent development of speech analysis technology and speech recognition technology, conversation systems using a man-machine interface that accepts a user's natural speech as an input and makes a predetermined response to this input are becoming widespread. As an example of such a conversation system, there has been proposed a conversation system that establishes a conversation close to a natural conversation between humans and machines, taking into consideration the history of conversation, transition of topics, etc., rather than just keyword matching ( For example, Patent Document 1).

As a business / applied technology using such a conversation system, entertainment using the conversation system (for example, home and business games) can be considered. As an example, it is possible to have a conversation with a character (virtual personality) displayed on a monitor, and to have a game software that advances the game based on the conversation, or to have a conversation with the character while playing the game (the content of the conversation is the outcome of the game) It is considered to incorporate a conversation system as a secondary component in a main game such as game software that does not affect the game.
JP 2002-358304 A

  In the conventional conversation system as described above, the dialogue with the character is determined by the content of the user's utterance. However, in an actual person-to-person conversation, not only the content of the utterance but also whether the conversation is active, whether it tends to be silent, that is, the number of utterances within a predetermined period, or the conversation In many cases, the time interval of the conversation affects the emotions of the speaker and influences the subsequent conversation.

  In conventional conversation systems, whether or not such conversations are active is not reflected in the content of the conversation or the game played together with the conversation, causing unnaturalness in the dialogue with the character. There was a point.

  As means for solving the above problems, the present invention has the following features.

  A first aspect of the present invention is a game machine (game system) having a conversation processing unit (conversation control device) that outputs an answer in response to an utterance from a user and a game processing unit (game device) that executes a game. As suggested.

  Here, the “utterance” does not necessarily have to be voice, but may be character string data generated by a character input means such as a keyboard. The “answer” may be made either by voice or a character string displayed on the screen. In the present invention, the “game” includes not only a game for entertainment but also a game having a purpose other than entertainment such as an educational purpose and a research purpose.

  In the above game machine, the conversation processing unit stores a conversation database means (conversation database) for storing a plurality of topic specifying information, a conversation history determined by a previous conversation and a previous answer, and topic specification information determined by the conversation history. And a speech control unit (conversation control unit) for selecting an answer sentence by comparing the speech information and a counting unit (speech information number counting unit) for counting the number of speech information received within a predetermined period The game processing unit executes an event associated with the number of utterance information based on the number of utterance information counted by the counting means.

  Here, the “utterance information” refers to a set of words having a single unit uttered by the user, for example, a single sentence (including abbreviated sentences in which some are omitted). “Topic identification information” is information for grasping the theme of conversation between the character played by the user and the game machine.

  According to the present invention, since the game can be controlled so that various events occur according to the number of utterance information uttered by the user, it is possible to make the user feel more interesting.

In the above game machine, the counting means counts the number of utterance information when the conversation control means collates the topic specifying information determined by the discourse history and the utterance information and selects any one of the topic specifying information. You may do it.
With such a configuration, even if the user lists topics or meanings that have nothing to do with the topic at all, it will not be counted as the number of utterance information, and only for conversations that have a flow. You can control the game so that various events occur based on it.

  The game machine further includes emotion state information management means for managing emotion state information for controlling the emotion of the character provided by the game machine as a user's partner. The number of conversation information obtained as a result of counting may be received from the counting means, and predetermined processing of emotion state information may be performed according to the number of conversation information.

  According to this configuration, since the user's positive conversation attitude (the number of utterance information is large) and the user's passive utterance attitude (the number of utterance information is small) can be reflected in the occurrence of the game event, Due to the synergistic effect with the answer of the conversation control means, it is possible to produce a more natural character.

  According to the present invention, since the game can be controlled so that various events occur according to the number of utterance information uttered by the user, it is possible to make the user feel more interesting.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. The present embodiment relates to a game system that can accept a user's voice. Hereinafter, this game system will be described.

[Game system configuration example]
FIG. 1 is a block diagram illustrating a configuration example of a game system. The game system GS includes a conversation control device 1 and a game device 2. The game system GS further includes a voice input unit 3 connected to the conversation control device 1 and the game device 2, a non-voice input unit 4 connected to the game device 2, and an image output unit 5 connected to the game device 2. And a voice output means 6 connected to the conversation control device 1 and the game device 2.

  The conversation control device 1 has a function of returning an answer according to the user's utterance so that a conversation between the user and the conversation control device 1 is established.

  The game apparatus 2 has a function of executing a predetermined game process in response to a user input and presenting an execution result to a user (user). Note that any type of game may be handled by the game system GS, but in the present embodiment, a game apparatus 2 that handles a mahjong game that is a competitive game will be described.

  Each of the conversation control device 1 and the game device 2 is an information processing device such as a computer or a workstation, for example, an arithmetic processing unit (CPU), a main memory (RAM), a read only memory (ROM), an input / output device (I). / O), and a device having an external storage device such as a hard disk device. A program for causing an information processing device to function as the conversation control device 1 and / or the game device 2 in the ROM or an external storage device, or a program for causing a computer to execute the conversation control method and / or the game execution method. The conversation control device 1 and / or the game device 2 is realized by placing the program on the main memory and executing it by the CPU. In addition, the program does not necessarily have to be stored in a storage device in the apparatus, and a computer-readable program recording medium such as a magnetic disk, an optical disk, a magneto-optical disk, a CD (Compact Disc), a DVD (Digital Video Disc), etc. Alternatively, it may be provided from an external device (for example, a server of an ASP (Application Service Provider)), and this may be placed on the main memory.

  In FIG. 1, the conversation control device 1 and the game device 2 are displayed as independent devices. However, the conversation control device 1 and the game device 2 do not have to be independent from each other. (CPU), main memory (RAM), read-only memory (ROM), input / output device (I / O), external storage device such as a hard disk device, etc. are used in common, and the conversation control device 1 and the game with the same hardware A configuration for realizing the device 2 may be used.

  The voice input means 3 has a function of converting a user's voice signal into a predetermined signal such as an electric signal or an optical signal and supplying the signal to the conversation control device 1 and the game device 2, and is, for example, a voice input microphone. The voice input means 3 accepts an input related to a game (for example, pong, reach, or long) as well as an utterance input to a pseudo personality (character) realized by the conversation control device 1 by a program or the like.

  The non-speech input unit 4 has a function of providing the game apparatus 2 with input by the user other than the voice input, and is a keyboard, a joystick, a controller, a pointing device, or the like, for example. Thereby, the user can inform the game apparatus 2 of input necessary for the game, for example, to make a bag, throw away a bag, pong, reach, or run.

  The image output means 5 has a function of displaying to the user a game screen generated by the game apparatus 2 as the game progresses, and is, for example, a liquid crystal display apparatus.

  The voice output means 6 outputs the content output as an answer sentence of the character (pseudo personality, virtual personality) by the conversation control device 1 as a voice signal, and also outputs voice / sound (sound effect, BGM, etc.) related to the game. For example, a sound board or a speaker.

[Game device]
Next, a configuration example of the game apparatus 2 will be described with reference to FIG.
As described above, the game apparatus 2 includes an external storage device such as an arithmetic processing unit (CPU), a main memory (RAM), a read-only memory (ROM), an input / output device (I / O), and a hard disk device. By executing a predetermined program on the information processing apparatus, the game apparatus 2 and the following components constituting the game apparatus 2 are realized.
FIG. 2 is a block diagram illustrating a configuration example of the game apparatus 2. The game apparatus 2 includes a character string / command conversion unit 201, a game progress control unit 202, an image processing unit 203, and a sound processing unit 204.

  The character string / command conversion unit 201 has a function of converting the user's utterance sent from the conversation control device 1 into character commands, and for example, when the user utters “Pon!” The command conversion unit 201 changes the character string information “Pong” sent from the conversation control device 1 to an execution command of “Pong” and passes it to the game progress control unit 202. It should be noted that, out of the user's speech, those not related to the progress of the game ( "Hello", "name?") For the string / instruction conversion unit 201 is not the any of the output.

The game progress control unit 202 has a function of causing the game to proceed in accordance with user input and designating data for screen display and data for audio output according to the progress.
The image processing unit 203 reads the screen display data designated by the game progress control unit 202 from a storage unit (not shown) that stores image data necessary for the game in advance, and stores the data in the image output unit 5. provide.
The voice processing unit 204 reads out data for screen display designated by the game progress control unit 202 from a storage unit (not shown) that stores voice data / acoustic data necessary for the game in advance, and outputs this as voice output. Provided to means 6.

  In addition, when the game progress control unit 202 receives a game end command from the conversation control device 1, the game progress control unit 202 operates to end the game.

[Configuration example of conversation control device]
[overall structure]
FIG. 3 is a schematic configuration diagram of the conversation control device 1 according to the present embodiment.
As described above, the conversation control device 1 includes an external storage device such as an arithmetic processing unit (CPU), a main memory (RAM), a read-only memory (ROM), an input / output device (I / O), and a hard disk device. The following components constituting the conversation control device 1 and the conversation control device 1 are realized by executing a predetermined program by the information processing device.
As shown in FIG. 3, the conversation control device 1 includes a speech recognition unit 200, a conversation control unit 300, a sentence analysis unit 400, a conversation database 500, an emotional state information management unit 600, and a speech recognition dictionary storage unit 700. And an utterance information count unit 800.

[Voice recognition part]
The voice recognition unit 200 specifies a character string corresponding to the utterance content based on the signal corresponding to the utterance provided from the voice input unit 3. Specifically, the voice recognition unit 200 to which the voice signal is input from the voice input unit 3 collates this voice signal with the dictionary and the conversation database 500 stored in the voice recognition dictionary storage unit 700 to obtain the voice signal. The speech recognition result estimated from the above is output. In the configuration example illustrated in FIG. 3, the speech recognition unit 200 requests the conversation control unit 300 to acquire the storage content of the conversation database 500, and the storage content of the conversation database 500 acquired by the conversation control unit 300 in response to the request. However, the voice recognition unit 200 may directly acquire the stored contents of the conversation database 500 and compare it with the voice signal.

[Configuration example of voice recognition unit]
FIG. 4 is a functional block diagram illustrating a configuration example of the voice recognition unit 200. The speech recognition unit 200 includes a feature extraction unit 200A, a buffer memory (BM) 200B, a word matching unit 200C, a buffer memory (BM) 200D, a candidate determination unit 200E, and a word hypothesis narrowing unit 200F. Yes. The word matching unit 200C and the word hypothesis narrowing unit 200F are connected to the speech recognition dictionary storage unit 700, and the candidate determination unit 200E is connected to the conversation control unit 300.

  The speech recognition dictionary storage unit 700 connected to the word matching unit 200C stores phoneme hidden Markov models (hereinafter, hidden Markov models are referred to as HMMs). The phoneme HMM is represented including each state, and each state has the following information. (A) state number, (b) acceptable context class, (c) list of preceding and subsequent states, (d) parameters of output probability density distribution, and (e) self-transition probabilities and transitions to subsequent states. It consists of probabilities. Note that the phoneme HMM used in the present embodiment is generated by converting a predetermined speaker mixed HMM because it is necessary to specify which speaker each distribution is derived from. Here, the output probability density function is a mixed Gaussian distribution having a 34-dimensional diagonal covariance matrix. In addition, the speech recognition dictionary storage unit 700 connected to the word matching unit 200C stores a word dictionary. The word dictionary stores a symbol string indicating a symbolic reading for each word of the phoneme HMM.

  The voice of the speaker is input to a microphone or the like and converted into an audio signal, and then input to the feature extraction unit 200A. The feature extraction unit 200A performs A / D conversion on the input audio signal, extracts feature parameters, and outputs them. There are various methods for extracting and outputting feature parameters. For example, as an example, LPC analysis is performed, and logarithmic power, 16th-order cepstrum coefficient, Δlogarithmic power, and 16th-order Δcepstrum coefficient are calculated. Examples include a method of extracting 34-dimensional feature parameters. The extracted time series of feature parameters is input to the word matching unit 200C via the buffer memory (BM) 200B.

  The word matching unit 200C uses the one-pass Viterbi decoding method to generate a phoneme HMM and a word dictionary stored in the speech recognition dictionary storage unit 700 based on feature parameter data input via the buffer memory 200B. Is used to detect the word hypothesis, and the likelihood is calculated and output. Here, the word matching unit 200C calculates the likelihood in the word and the likelihood from the start of utterance for each state of each HMM at each time. The likelihood is individually provided for each word identification number, word start time, and difference between preceding words. In order to reduce the amount of calculation processing, the low likelihood grid hypothesis among the total likelihoods calculated based on the phoneme HMM and the word dictionary may be reduced. The word matching unit 200C includes information on the detected word hypothesis and its likelihood along with time information from the utterance start time (specifically, for example, a frame number) and the candidate determination unit 200E and the word hypothesis narrowing down via the buffer memory 200D. Output to the unit 200F.

  The candidate determining unit 200E refers to the conversation control unit 300, compares the detected word hypothesis with the topic specifying information in the predetermined discourse space, and among the detected word hypotheses, the topic specifying information in the predetermined discourse space If there is a match, the matching word hypothesis is output as a recognition result. If there is no match, the word hypothesis narrowing unit 200F Request to narrow down hypotheses.

  An operation example of the candidate determination unit 200E will be described. Now, the word matching unit 200C outputs a plurality of word hypotheses “Kantaku”, “Katak”, “Kantoku” and the likelihood (recognition rate), and the predetermined discourse space relates to “movie”, and the topic specifying information includes “Kantoku (director)” is included, but “Kantaku (reclaimed)” and “Katak (temporary contract)” are not included. Further, the likelihood (recognition rate) of “Kantaku”, “Katak”, and “Kantoku” is highest in “Kantaku”, lowest in “Kantoku”, and “Katak” is in between.

  In the above situation, the candidate determining unit 200E compares the detected word hypothesis with the topic specifying information in the predetermined discourse space, and the word hypothesis “Kantoku” matches the topic specifying information in the predetermined discourse space. The word hypothesis “Kantoku” is output as a recognition result and passed to the conversation control unit. By processing in this way, “Kantoku (Director)” related to the topic “Movie” currently being handled is prioritized over the word hypotheses “Kantaku” and “Katak” with higher likelihood (recognition rate). As a result, it is possible to output a speech recognition result in accordance with the context of the conversation.

  On the other hand, if there is no match, the word hypothesis narrowing unit 200F operates to output the recognition result in response to a request for narrowing down word hypotheses from the candidate determination unit 200E. The word hypothesis narrowing-down unit 200F refers to a statistical language model stored in the speech recognition dictionary storage unit 700 based on a plurality of word hypotheses output from the word matching unit 200C via the buffer memory 200D. For the same word hypothesis of the same word with the same end time but different start time, the highest likelihood of the calculated total likelihood from the utterance start time to the end time of the word for each head phoneme environment of the word After narrowing down word hypotheses so that they are represented by a single word hypothesis having a degree, a word string of a hypothesis having the maximum total likelihood is output as a recognition result among the word strings of all the word hypotheses after narrowing down To do. In the present embodiment, preferably, the first phoneme environment of the word to be processed is three phonemes including the final phoneme of the word hypothesis preceding the word and the first two phonemes of the word hypothesis of the word. Say a line.

An example of word narrowing processing by the word hypothesis narrowing unit 200F will be described with reference to FIG. FIG. 5 is a timing chart showing an example of processing of the word hypothesis narrowing-down unit 200F.
For example, when the i-th word Wi consisting of the phoneme sequence a1, a2,..., An comes after the (i-1) -th word Wi-1, six hypotheses Wa as word hypotheses Wa, Assume that Wb, Wc, Wd, We, and Wf exist. Here, it is assumed that the final phoneme of the former three word hypotheses Wa, Wb, and Wc is / x /, and the final phoneme of the latter three word hypotheses Wd, We, and Wf is / y /. Assuming that three hypotheses premised on the word hypotheses Wa, Wb, and Wc and one hypothesis premised on the word hypotheses Wd, We, and Wf remain at the end time te, the former three with the same initial phoneme environment Of the hypotheses, one of the hypotheses with the highest total likelihood is left and the others are deleted.

  Note that the hypothesis premised on the word hypothesis Wd, We, Wf is that the leading phoneme environment is different from the other three hypotheses, that is, the final phoneme of the preceding word hypothesis is y instead of x, so this word hypothesis Wd , We and Wf are not deleted. That is, only one hypothesis is left for each final phoneme of the preceding word hypothesis.

In the above embodiment, the first phoneme environment of the word is defined as three phoneme sequences including the final phoneme of the word hypothesis preceding the word and the first two phonemes of the word hypothesis of the word. However, the present invention is not limited to this, the phoneme string of the preceding word hypothesis including the final phoneme of the preceding word hypothesis, and at least one phoneme of the preceding word hypothesis continuous with the last phoneme, and the word hypothesis of the word A phoneme sequence including a phoneme string including the first phoneme may be used.
In the above embodiment, the feature extraction unit 200A, the word collation unit 200C, the candidate determination unit 200E, and the word hypothesis narrowing unit 200F are configured by, for example, a computer such as a digital computer, and the buffer memory 200B, The 200D and the speech recognition dictionary storage unit 700 are configured by a storage device such as a hard disk or a memory.

In the above embodiment, speech recognition is performed using the word matching unit 200C and the word hypothesis narrowing unit 200F. However, the present invention is not limited to this, and for example, a phoneme matching unit that refers to a phoneme HMM, One
You may comprise with the speech recognition part which performs speech recognition of a word with reference to a statistical language model using a Pass DP algorithm.
In the present embodiment, the voice recognition unit 200 is described as a part of the conversation control device 1, but an independent voice recognition device including the voice recognition unit 200, the voice recognition dictionary storage unit 700, and the conversation database 500. It is also possible.

[Operation example of voice recognition unit]
Next, the operation of the speech recognition unit 200 will be described with reference to FIG. FIG. 6 is a flowchart showing an operation example of the speech recognition unit 200. When receiving a voice signal from the voice input unit 3, the voice recognition unit 200 performs a feature analysis of the input voice and generates a feature parameter (step S601). Next, this feature parameter is compared with the phoneme HMM and language model stored in the speech recognition dictionary storage unit 700 to obtain a predetermined number of word hypotheses and their likelihoods (step S602). Next, the speech recognition unit 200 compares the acquired predetermined number of word hypotheses with the detected word hypothesis and the topic identification information in the predetermined discourse space, and among the detected word hypotheses, the topic in the predetermined discourse space It is determined whether there is a match with the specific information (steps S603 and S604). If there is a match, the speech recognition unit 200 outputs the matching word hypothesis as a recognition result (step S605). On the other hand, when there is no match, the speech recognition unit 200 outputs the word hypothesis having the maximum likelihood as the recognition result according to the acquired likelihood of the word hypothesis (step S606).

[Voice recognition dictionary storage]
Returning to FIG. 3 again, the description of the configuration example of the conversation control device 1 will be continued.
The voice recognition dictionary storage unit 700 stores a character string corresponding to a standard voice signal. The voice recognition unit 200 that has performed the collation identifies a character string corresponding to the word hypothesis corresponding to the voice signal, and outputs the identified character string to the conversation control unit 300 as a character string signal.

[Sentence Analysis Department]
Next, a configuration example of the sentence analysis unit 400 will be described with reference to FIGS. 1 and 7. FIG. 7 is a partial enlarged block diagram of the conversation control device 1, and is a block diagram illustrating a specific configuration example of the conversation control unit 300 and the sentence analysis unit 400. In FIG. 7, only the conversation control unit 300, the sentence analysis unit 400, and the conversation database 500 are shown, and the display of other components is omitted.

  The sentence analysis unit 400 analyzes the character string specified by the speech recognition unit 200. In this embodiment, as shown in FIG. 7, the sentence analysis unit 400 includes a character string identification unit 410, a morpheme extraction unit 420, a morpheme database 430, an input type determination unit 440, and an utterance type database 450. have. The character string specifying unit 410 divides a series of character strings specified by the voice input unit 3 and the voice recognition unit 200 for each sentence. This one-sentence means a delimiter sentence in which character strings are divided as finely as possible without breaking the meaning of the grammar. Specifically, when there is a certain time interval or more in a series of character strings, the character string specifying unit 410 divides the character string at that portion. The character string specifying unit 410 outputs the divided character strings to the morpheme extracting unit 420 and the input type determining unit 440. It should be noted that “character string” described below means a character string for each phrase.

[Morpheme extraction unit]
The morpheme extraction unit 420 sets, as first morpheme information, each morpheme constituting the minimum unit of the character string from the character string of the one phrase according to the character string of the one sentence divided by the character string specifying unit 410. To extract. Here, in this embodiment, the morpheme means the minimum unit of the word structure represented in the character string. Examples of the minimum unit of the word structure include parts of speech such as nouns, adjectives and verbs.

  FIG. 8 is a diagram illustrating a relationship between a character string and a morpheme extracted from the character string. As shown in FIG. 8, each morpheme can be expressed as m1, m2, m3... In the present embodiment. As shown in FIG. 8, the morpheme extraction unit 420 to which the character string is input from the character string specifying unit 410 includes the input character string and a morpheme group stored in advance in the morpheme database 430. Morphemes that belong to the part-of-speech classification are prepared as a morpheme dictionary that describes the morpheme entry word, reading, part-of-speech, utilization form, etc.). The collated morpheme extraction unit 420 extracts each morpheme (m1, m2,...) That matches one of the previously stored morpheme groups from the character string. Examples of the elements (n1, n2, n3...) Excluding each extracted morpheme include auxiliary verbs.

  The morpheme extraction unit 420 outputs each extracted morpheme to the topic identification information search box 320 as first morpheme information. Note that the first morpheme information need not be structured. Here, “structured” means to classify and arrange morphemes contained in a character string based on the part of speech, for example, a character string that is an utterance sentence, such as “subject + object + predicate”. In the same way, it refers to conversion into data obtained by arranging morphemes in a predetermined order. Of course, even if structured first morpheme information is used, this does not interfere with the implementation of the present embodiment.

[Input type determination unit]
The input type determination unit 440 has a function of determining the type of utterance content (speech type) based on the character string specified by the character string specifying unit 410 and outputting information indicating the utterance type according to the determination result. This utterance type is information for specifying the type of utterance content, and in the present embodiment, it means, for example, the “spoken sentence type” shown in FIG. FIG. 9 is a diagram illustrating an example of “spoken sentence type”, a two-letter alphabet representing the type of the spoken sentence, and a spoken sentence corresponding to the type of the spoken sentence.

  In this embodiment, as shown in FIG. 9, “spoken sentence type” is a statement sentence (D; Declaration), a time sentence (T; Time), a location sentence (L; Location), and a repulsive sentence. (N; Negation) and the like, and is used to determine the answer sentence according to the type of the utterance sentence. The sentence composed of each type is composed of an affirmative sentence or a question sentence. The “declaration sentence” means a sentence indicating a user's opinion or idea. In the present embodiment, this statement is a sentence such as “I like Sato” as shown in FIG. “Place sentence” means a sentence with a place concept. “Time sentence” means a sentence with a temporal concept. “Rebound sentence” means a sentence when a statement is denied. An example sentence for “spoken sentence type” is as shown in FIG.

  In the case of determining “spoken sentence type”, in this embodiment, the input type determining unit 440 is a definition expression dictionary for determining that it is a statement sentence, and a repulsive expression for determining that it is a repulsive sentence. A dictionary group describing the correspondence between the expression of the dictionary and the type of utterance is used. FIG. 10 shows a dictionary to be used and types of determinations that are made when the corresponding expression is included in the dictionary. As a specific example, the input type determination unit 440 to which a character string is input from the character string specifying unit 410, based on the input character string, each character string and each dictionary stored in the utterance type database 450, Is matched. The input type determination unit 440 that has performed the collation extracts elements (data indicating the type of determination such as D and N) related to each dictionary from the character string.

  The input type determination unit 440 determines “spoken sentence type” based on the extracted elements. For example, when an element that describes a certain event is included in a character string, the input type determination unit 440 determines the character string that includes the element as a statement. The input type determination unit 440 outputs the determined “spoken sentence type” to the answer acquisition unit 350.

[Conversation database]
Next, a data configuration example of data stored in the conversation database 500 will be described with reference to FIG. FIG. 11 is a conceptual diagram illustrating an example of a data configuration of data stored in the conversation database 500.

    [Topic specific information]

  As shown in FIG. 11, the conversation database 500 stores a plurality of pieces of topic specifying information 810 for specifying topics in advance. Each topic specifying information 810 may be associated with other topic specifying information. For example, in the example shown in FIG. 11, when the topic specifying information C is specified, it is associated with the topic specifying information C. The other topic specifying information A, B, D is stored so as to be determined.

  Specifically, in the present embodiment, the topic identification information 810 means “keywords” that are relevant to the input content expected to be input by the user or the answer sentence to the user.

  One or more topic titles 820 are stored in the topic specifying information 810 in association with each other. The topic title 820 is composed of morphemes composed of one character, a plurality of character strings, or a combination thereof. Each topic title 820 stores an answer sentence 830 to the user in association with it. In addition, a plurality of answer types indicating the type of the answer sentence 830 are associated with the answer sentence.

  Furthermore, an emotion flag 840 that is data indicating the emotion of the pseudo personality provided by the conversation control device 1 is stored in association with the answer sentence 830. The emotion flag 840 may be any data as long as it can distinguish emotions. For example, the character data “A” is assigned as an emotion flag indicating “normal” emotion and the emotion indicating “anger” emotion is indicated. The character data “B” is assigned as the flag 840, the character data “C” is assigned as the emotion flag indicating the emotion of “anger”, the character data “D” is assigned as the emotion flag indicating the emotion of “joy”, and so on. To do. The conversation control device 1 according to the present embodiment refers to the emotion flag 840 and controls the emotion of the pseudo personality provided by the conversation control device 1.

Next, the association between certain topic specifying information and other topic specifying information will be described. FIG. 12 is a diagram showing an association between certain topic specifying information 810A and other topic specifying information 810B, 810C _{1 to} 810C ₄ , 810D _{1 to} 810D ₃ . In the following description, “stored in association” means that when information X is read, information Y associated with the information X can be read. For example, information Y in the data of the information X Is stored as information (for example, a pointer indicating the storage destination address of information Y, a physical memory address of the storage destination of information Y, and a logical address). "Remembered".

  In the example shown in FIG. 12, the topic identification information can be stored in association with other topic identification information in association with a higher concept, a lower concept, a synonym, and a synonym (omitted in the example of this figure). In the example shown in this figure, topic specifying information 810B (= “entertainment”) is stored in association with the topic specifying information 810A as topic specifying information of the higher concept for the topic specifying information 810A (= “movie”). The topic specific information (“movie”) is stored in the upper hierarchy.

Further, topic specific information 810C ₁ (= “director”), topic specific information 810C ₂ (= “starring”), topic specific information 810C ₃ (= “distribution company” for topic specific information 810A (= “movie”) )), Topic identification information 810C ₄ (= “screening time”), topic identification information 810D ₁ (= “Seven Samurai”), topic identification information 810D ₂ (= “Ran”), topic identification information 810D ₃ ( = "Bouncer"), ... are stored in association with the topic identification information 810A.

  In addition, the synonym 900 is associated with the topic identification information 810A. In this example, “works”, “contents”, and “cinema” are stored as synonyms of the keyword “movie” that is the topic identification information 810A. By defining such synonyms, the topic specifying information 810A is obtained when the utterance does not include the keyword “movie” but includes “works”, “contents”, and “cinema” in the utterance sentence or the like. Can be handled as being included in an utterance sentence.

  When the conversation control device 1 according to the present embodiment identifies certain topic identification information by referring to the stored content of the conversation database 500, the other topic identification information stored in association with the topic identification information and It is possible to search and extract topic titles and answer sentences of topic identification information at high speed.

[Topic title]
Next, a data configuration example of a topic title (also referred to as “second morpheme information”) will be described with reference to FIG. FIG. 13 is a diagram illustrating a data configuration example of a topic title (also referred to as “second morpheme information”).

The topic identification information 810D ₁ , 810D ₂ , 810D ₃ ,... Has a plurality of different topic titles 820 ₁ , 820 ₂ ,..., Topic titles 820 ₃ , 820 ₄ ,..., Topic titles 820 ₅ , 820 ₆ ,. ing.
In the present embodiment, as shown in FIG. 13, each topic title 820 is information including first specific information 1301, second specific information 1302, and third specific information 1303. Here, in the present embodiment, the first specific information 1301 means main morphemes constituting a topic. As an example of the first specific information, for example, a subject constituting a sentence can be cited. Further, the second specific information 1302 means a morpheme having a close relationship with the first specific information 1301 in the present embodiment. The second specific information 1302 is, for example, an object. Further, in the present embodiment, the third specifying information 1303 means a morpheme that indicates movement of a certain object or a morpheme that modifies a noun or the like. Examples of the third specific information 1303 include verbs, adverbs, and adjectives. The meanings of the first specific information 1301, the second specific information 1302, and the third specific information 1303 do not have to be limited to the above-described contents, and other meanings are the first specific information 1301, the second specific information 1302, Even if the third specific information 1303 is given, the present embodiment is established as long as the contents of the sentence can be grasped from these.

For example, the subject is "Seven Samurai" and the adjective is "interesting", as shown in FIG. 13, the topic title (second morpheme information) 820 ₂ is the first identification information 1301 morpheme " It consists of “Seven Samurai” and the morpheme “Funny” which is the third specific information 1303. Incidentally, this is the topic title 820 ₂ not included morpheme is the second specifying information, the symbol for indicating that there is no corresponding morpheme "*" is stored as the second specification information 1302.

The topic title 820 ₂ (Seven Samurai; *; Interesting) has the meaning of “Seven Samurai is interesting”. In the present specification, the parentheses constituting the topic title 820 are in the order of first specific information 1301, second specific information 1302, and third specific information 1303 from the left in the following. In addition, in the topic title 820, when there is no morpheme included in the first to third specific information, “*” is indicated for the portion.

  Note that the specific information constituting the topic title 820 is not limited to three like the first to third specific information as described above, and is further limited to other specific information (fourth specific information, and it). You may make it have the above.

[Answer]
Next, the answer sentence 830 will be described. As shown in FIG. 14, in the present embodiment, the reply sentence 830 includes a statement (D; Declaration) and a time (T; Time) in order to make a reply corresponding to the type of utterance sentence uttered by the user. , Location (L; Location), negation (N; Negation), and other types (answer types). The affirmative sentence is “A” and the question sentence is “Q”.
For example, when the topic title (820) 1-1 is (Sato; *; likes) {this is an extracted morpheme contained in “I like Sato”}, the topic title (820) 1 The answer sentence (830) 1-1 corresponding to -1 is (DA; statement affirmation sentence "I also like Sato"), (TA; time affirmation sentence "I like Sato when I was standing at bat" ]). An answer acquisition unit 350 described later acquires one answer sentence 830 associated with the topic title 820.

[Conversation control unit]
Here, returning to FIG. 7, a configuration example of the conversation control unit 300 will be described.

The conversation control unit 300 includes each component (speech recognition unit 200, sentence analysis unit 400, conversation database 500, emotion state information management unit 600, speech recognition dictionary storage unit 700, utterance information count unit 800 in the conversation control device 1. ) And a function for determining and outputting an answer sentence that responds to an utterance.
In the present embodiment, as shown in FIG. 7, the conversation control unit 300 includes a management unit 310, a topic specifying information search unit 320, an abbreviated sentence complement unit 330, a topic search unit 340, and an answer acquisition unit 350. And have. The management unit 310 controls the entire conversation control unit 300 and notifies the utterance information count unit 800 every time utterance information is generated. In addition, when the management unit 310 detects the occurrence of utterance information, the number of utterance information is counted for each occurrence of utterance information using a character string or morpheme specified by the sentence analysis unit 400 with reference to a sentence break (punctuation mark) or the like. The unit 800 may be notified.

The management unit 310 has a function of storing the discourse history and updating it as necessary. In response to requests from the topic identification information search unit 320, the abbreviated sentence complement unit 330, the topic search unit 340, and the answer acquisition unit 350, the management unit 310 converts all or part of the stored discourse history into these units. The function to pass to.
The “discourse history” is information for specifying the topic and subject of the conversation between the user and the conversation control device 1, and the discourse history to be described later is “target topic specification information”, “target topic title”, “user input sentence topic” This information includes at least one of “specific information” and “answer sentence topic specific information”. In addition, “focused topic identification information”, “focused topic title”, and “answer sentence topic specific information” included in the discourse history are not limited to those determined by the previous conversation, but focused topic identification information during a past predetermined period. "Remarked topic title", "Reply sentence topic specific information", or a cumulative record thereof.

  Hereinafter, each part which comprises the conversation control part 300 is demonstrated.

[Topic specific information search part]
The topic identification information search unit 320 collates the first morpheme information extracted by the morpheme extraction unit 420 with each topic identification information, and the topic that matches the morpheme constituting the first morpheme information from each topic identification information. Search for specific information. Specifically, the topic identification information search unit 320, when the first morpheme information input from the morpheme extraction unit 420 is composed of two morphemes "Sato" and "like", the input first morpheme The information is collated with the topic specific information group.

  The topic identification information search unit 320 that has performed this collation, when a morpheme constituting the first morpheme information (for example, “Sato”) is included in the focused topic title 820focus (topic title searched up to the previous time), The subject topic title 820focus is output to the answer acquisition unit 350. On the other hand, when the morpheme constituting the first morpheme information is not included in the focused topic title 820focus, the topic identification information search unit 320 determines the user input sentence topic identification information based on the first morpheme information and inputs it. The first morpheme information and the user input sentence topic specifying information are output to the abbreviated sentence complementing unit 330. "User input sentence topic specific information" is included in the topic specific information corresponding to the morpheme corresponding to the content that the user is talking about or the first morpheme information among the morphemes included in the first morpheme information. The topic specific information corresponding to the morpheme which may correspond to the content which the user is talking about among morphemes.

[Abbreviated sentence completion part]
The abbreviated sentence complementing unit 330 uses the first morpheme information as the topic specifying information 810 (hereinafter referred to as “focused topic specifying information”) searched up to the previous time and the topic specifying information 810 (hereinafter referred to as “ A plurality of types of complemented first morpheme information is generated by complementing using “answer sentence topic specifying information”). For example, when the utterance sentence is a sentence “I like”, the abbreviated sentence complementing unit 330 includes the topic topic identification information “Sato” in the first morpheme information “like” and the complemented first morpheme information “ "Sato likes".

  In other words, if the first morpheme information is “W” and the set of the topic topic identification information and the answer sentence topic specification information is “D”, the abbreviated sentence complementing unit 330 adds the set “D” to the first morpheme information “W”. Complemented first morpheme information including elements is generated.

  As a result, when the sentence constructed using the first morpheme information is an abbreviated sentence and is not clear as Japanese, the abbreviated sentence complementing unit 330 uses the set “D” to set the set “D”. (For example, “Sato”) can be included in the first morpheme information “W”. As a result, the abbreviated sentence complementing unit 330 can change the first morpheme information “like” to the first morpheme information “Sato, like”. The complemented first morpheme information “Sato, I like” corresponds to the utterance content “I like Sato”.

  That is, the abbreviated sentence complementing unit 330 can supplement the abbreviated sentence using the set “D” even when the user's utterance content is an abbreviated sentence. As a result, even if the sentence composed of the first morpheme information is an abbreviated sentence, the abbreviated sentence complementing unit 330 can make the sentence in proper Japanese.

  In addition, the abbreviated sentence complementing unit 330 searches for the topic title 1020 that matches the first morpheme information after completion based on the set “D”. If a topic title 1020 that matches the first morpheme information after complement is found, the abbreviated sentence complement unit 330 outputs the topic title 1020 to the answer acquisition unit 350. The answer acquisition unit 350 can output the answer sentence 1030 most suitable for the user's utterance content based on the appropriate topic title 1020 searched by the abbreviation sentence complementing unit 330.

  Note that the abbreviated sentence complementing unit 330 is not limited to only including elements of the set “D” in the first morpheme information. The abbreviated sentence complementing unit 330 extracts the first morpheme extracted from the morpheme included in any one of the first specific information, the second specific information, or the third specific information constituting the topic title based on the topic title of interest. It may be included in the information.

[Topic Search Department]
When the topic title 820 is not determined by the abbreviated sentence complementing unit 330, the topic search unit 340 collates the first morpheme information with each topic title 820 corresponding to the user input sentence topic specifying information, and each topic title 820 The topic title 820 that is most suitable for the first morpheme information is searched for.
Specifically, the topic search unit 340 to which the search command signal is input from the abbreviated sentence complement unit 330 is used based on the user input sentence topic identification information and the first morpheme information included in the input search command signal. The topic title 820 most suitable for the first morpheme information is searched from among the topic titles associated with the person input sentence topic identification information. The topic search unit 340 outputs the searched topic title 820 to the answer acquisition unit 350 as a search result signal.

FIG. 15 is a diagram illustrating specific examples of the topic title 820, the answer sentence 830, and the emotion flag 840 associated with certain topic specifying information 810 (= “Sato”). As shown in FIG. 15, for example, the topic search unit 340 includes the topic identification information 810 (= “Sato”) in the input first morpheme information “Sato, I like”, so the topic identification information 810 (= "Sato") is specified, and then the first morpheme information inputted as each topic title (820) 1-1, 1-2, ... associated with the topic specifying information 810 (= "Sato") Match “Sato, I like”.
The topic search unit 340, based on the comparison result, the topic title (820) that matches the input first morpheme information “Sato, I like” from among the topic titles (820) 1-1 and 1-2. Specify 1-1 (Sato; *; likes). The topic search unit 340 outputs the searched topic title (820) 1-1 (Sato; *; likes) to the answer acquisition unit 350 as a search result signal.

[Answer section]
The answer acquisition unit 350 acquires an answer sentence 830 associated with the topic title 820 based on the topic title 820 searched by the topic search unit 340. Also, the answer acquisition unit 350 collates each answer type associated with the topic title with the utterance type determined by the input type determination unit 440 based on the topic title searched by the topic search unit 340. . The answer acquisition unit 350 that has performed the collation searches for an answer type that matches the determined utterance type from among the answer types.

As shown in FIG. 15, for example, when the topic title 820 searched by the topic search unit 340 is the topic title (820) 1-1 (Sato; Matches the “spoken sentence type” (for example, DA) determined by the input type determination unit 440 from among the answer sentences (830) 1-1 (DA, TA, etc.) associated with the title 1-1. Identify the answer type (DA). Based on the identified answer type (DA), the answer acquisition unit 350 that identified the answer type (DA) responds to the answer sentence (830) 1-1 associated with the answer type (DA) ("I am also Sato I like. ”) To get.
Here, among the “DA”, “TA”, etc., “A” means an affirmative form. Therefore, when “A” is included in the utterance type and the answer type, it indicates that a certain matter is affirmed. In addition, types such as “DQ” and “TQ” can be included in the utterance type and the answer type. Of these “DQ”, “TQ”, etc., “Q” means a question about a certain matter.

  When the answer type is the above question format (Q), the answer sentence associated with the answer type is configured in an affirmative format (A). Examples of the answer sentence created in this affirmative form (A) include a sentence that answers a question item. For example, when the utterance sentence is "Have you operated the slot machine?", The utterance type for this utterance sentence is a question form (Q). The answer sentence 830 associated with this question format (Q) is, for example, “I have operated the slot machine” (affirmative format (A)).

  On the other hand, when the utterance type is an affirmative form (A), the answer sentence 830 associated with the answer type is configured with a question form (Q). Examples of the answer sentence 830 created in this question format (Q) include a question sentence that is replied to the utterance content, or a question sentence that asks for a specific matter. For example, when the utterance sentence is “I play with a slot machine”, the utterance type for this utterance sentence is an affirmative form (A). The answer sentence associated with this affirmative form (A) is, for example, “isn't it a hobby to play with pachinko?” (Question sentence (Q) to ask for a specific matter).

  The answer acquisition unit 350 outputs the acquired answer sentence 830 as an answer sentence signal to the management unit 310 and outputs an emotion flag 840 corresponding to the answer sentence 830 to the emotion state information management unit 600. In addition, the management unit 310 to which the answer sentence signal is input from the answer acquisition unit 350 outputs the input answer sentence signal to the voice output unit 6 and the utterance information count unit 800 generates utterance information. To be notified.

[Speech information count section]
Next, the utterance information counting unit 800 will be described. The utterance information count section 800 counts the user's utterances within a predetermined period, stores the counted value as the utterance information number, selects an event according to the utterance information number during the predetermined period, and selects this as the game apparatus 2. It has a function to notify.

  FIG. 16 is a functional block diagram illustrating a configuration example of the utterance information count unit 800. The utterance information count unit 800 includes a predetermined period determination unit 800A, a counter unit 800B, and an event selection unit 800C.

  The predetermined period determination unit 800A has a function of detecting the start and end of a predetermined period and notifying the counter unit 800B. The counter unit 800B has a function of adding one by one to the count value every time notification of the generation of utterance information is received from the conversation control unit 300, and notifying the event selection unit of the count value when a predetermined period arrives. Upon receiving a notification of the start of the predetermined period from the predetermined period determination unit 800A, the counter unit 800B clears the stored count value so that a new count can be started.

  The event selection unit 800C has an event table in which the number of utterance information and events are stored in association with each other. When a count value is received from the counter unit 800B, the event selection unit 800C determines a corresponding event with reference to the event table. This has a function of notifying the game apparatus 2 of this.

  17A and 17B are diagrams showing examples of event tables. In FIG. 17A, two types of ending events to be executed after the game end are prepared. If the number of utterance information is 15 or more, a special event is selected, while the number of utterance information is less than 15. This is an event table for selecting a normal event different from a special event. The special ending event is composed of images, effects, and music that can enhance the user's satisfaction compared to the normal ending event. By using such an event table, the user can be encouraged to more actively interact with the game system GS so that the number of utterances can be increased so that a special ending event with a more satisfying feeling can be seen. .

FIG. 17B shows an example of an event table that defines events to be performed between the start of the game and the end of the game. In this event table, when the number of utterance information is less than 5, a bad event is selected, and when the number of utterance information is 5 or more and less than 12, normal event B is selected and the number of utterance information is 12 or more and less than 15. In this case, it is determined that the normal event A is selected, and that the special event is selected when the number of utterance information is 15 or more.
[Emotion State Information Management Department]
Returning to FIG. 3, the emotional state information management unit 600 will be described.

The emotion state information management unit 600 has a function of receiving the emotion flag 840 from the conversation control unit 300 and managing which emotion flag character data corresponding to. When emotion state information management unit 600 receives emotion flag 840 from conversation control unit 300, emotion state information management unit 600 stores the emotion flag 840, generates emotion state information based on the stored emotion flag, and sends emotion state information to game device 2 at a predetermined timing. To be notified. The emotional state information includes, for example, a value for each emotion (feeling emotional) and presence / absence of flag establishment. The emotional state information is information that determines the emotion of the opponent (pseudo personality, character) played by the game system GS, and the game system GS controls the display of character data by this emotional state information.
[Game system operation example]
Next, an operation example of the game system GS will be described.
FIG. 18 is a flowchart showing main processes for the conversation control device 1 of the game system GS according to the present embodiment. On the game device 2 side of the game system GS, processing related to the game is performed in parallel, but description thereof is omitted here.

  First, the conversation control device 1 accepts an utterance from the user by the voice recognition unit 200, and performs a user utterance acceptance process that changes to data that can be processed by the conversation control unit 300 (step S1801).

  Next, conversation control device 1 detects reception of utterance information by conversation control unit 300 and sends a count value (for example, one by one) to utterance information number counting unit 800 for each utterance information. The utterance information number counting unit 800 cumulatively adds these and performs user utterance number counting processing (step S1802).

  The conversation control device 1 checks whether or not a predetermined period has arrived (step S1803). If the predetermined period has not arrived, steps S1801 and S1802 are executed so that a new user can accept an utterance. On the other hand, when the predetermined period has arrived, an event corresponding to the number of utterance information is selected based on the event table described above using the count value that is the number of utterance information stored at that time (step S1804).

  Next, the conversation control device 1 notifies the game device 2 of the event selected in step S1804. The game device 2 performs a process for generating the notified event (step S1805). The event may be any event that can be executed by the game apparatus 2, for example, execution of ending, forced change of character, increase / decrease of points, forced termination of the game, execution of a predetermined effect, and the like.

  Next, the game system GS determines whether or not the game end condition is satisfied (step S1806). When the game end condition is satisfied (for example, the end of the game time), the game system GS ends the game. On the other hand, if the game end condition is not satisfied, the process returns to step S1801 and waits for acceptance of the user utterance.

  Concurrently with steps S1802 to S1805 or independently of these steps, the conversation control device 1 performs a conversation control process for making an answer to the user's utterance (step S1807).

[Conversation control method]
The conversation control process (step S1807) by the conversation control apparatus 1 having the above configuration can be implemented by the following procedure. FIG. 19 is a flowchart showing the procedure of the conversation control method according to the present embodiment.
First, the voice input means 3 performs a step of acquiring the utterance content from the user (step S1901). Specifically, the voice input unit 3 acquires the voice constituting the user's utterance content. The voice input unit 3 outputs the acquired voice to the voice recognition unit 200 as a voice signal.

  Next, the voice recognition unit 200 performs a step of specifying a character string corresponding to the utterance content based on the utterance content acquired by the voice input unit 3 (step S1902). Specifically, the voice recognition unit 200 to which a voice signal is input from the voice input unit 3 specifies a word hypothesis (candidate) corresponding to the voice signal based on the input voice signal. The voice recognition unit 200 acquires a character string associated with the identified word hypothesis (candidate), and outputs the acquired character string to the conversation control unit 300 as a character string signal.

  Then, the character string specifying unit 410 performs a step of dividing the series of character strings specified by the voice recognition unit 200 for each sentence (step S1903). Specifically, the character string specifying unit 410 to which a character string signal (or morpheme signal) is input from the management unit 310 has a certain time interval or more in the input series of character strings. , Delimit the string at that part. The character string specifying unit 410 outputs the divided character strings to the morpheme extracting unit 420 and the input type determining unit 440. In addition, when the input character string is a character string input from the keyboard, the character string specifying unit 410 preferably divides the character string at a part such as a punctuation mark or a space.

Thereafter, the morpheme extraction unit 420 performs a step of extracting each morpheme constituting the minimum unit of the character string as the first morpheme information based on the character string specified by the character string specifying unit 410 (step S1904). Specifically, the morpheme extraction unit 420 to which the character string is input from the character string specifying unit 410 collates the input character string with a morpheme group stored in advance in the morpheme database 430. In this embodiment, the morpheme group is prepared as a morpheme dictionary in which each morpheme belonging to each part-of-speech classification describes a morpheme entry word, reading, part-of-speech, utilization form, and the like.
The matched morpheme extraction unit 420 extracts each morpheme (m1, m2,...) That matches each morpheme included in the previously stored morpheme group from the input character string. The morpheme extraction unit 420 outputs each extracted morpheme to the topic identification information search unit 320 as first morpheme information.

  Next, the input type determination unit 440 performs a step of determining “spoken sentence type” based on each morpheme constituting one sentence specified by the character string specifying unit 410 (step S1905). Specifically, the input type determination unit 440, to which the character string is input from the character string specifying unit 410, determines the character string and each dictionary stored in the utterance type database 450 based on the input character string. Collation is performed, and elements related to each dictionary are extracted from the character string. The input type determination unit 440 that extracted this element determines to which “spoken sentence type” the element belongs based on the extracted element. The input type determination unit 440 outputs the determined “spoken sentence type” (speech type) to the answer acquisition unit 350.

Then, the topic identification information search unit 320 performs a step of comparing the first morpheme information extracted by the morpheme extraction unit 420 with the topic title of interest 820focus (step S1906).
If the morpheme constituting the first morpheme information matches the topic topic title 820focus, the topic identification information search unit 320 outputs the topic title 820 to the answer acquisition unit 350. On the other hand, if the morpheme constituting the first morpheme information and the topic title 820 do not match, the topic specific information search unit 320 searches the input first morpheme information and user input sentence topic specific information 810. An abbreviated sentence complementing unit 330 outputs the command signal.

  Thereafter, the abbreviated sentence complementing unit 330 includes the focused topic specifying information and the answer sentence topic specifying information in the input first morpheme information based on the first morpheme information input from the topic specifying information search unit 320. This is performed (step S1907). Specifically, when the first morpheme information is “W” and the set of the topic topic identification information and the answer sentence topic identification information is “D”, the abbreviated sentence complementing unit 330 identifies the topic as the first morpheme information “W”. Complemented first morpheme information including the element of information “D” is generated, and the complemented first morpheme information is collated with all topic titles 820 associated with the set “D” to be complemented. Whether there is a topic title 820 that matches the first morpheme information is searched. If there is a topic title 820 that matches the complemented first morpheme information, the abbreviated sentence complementing unit 330 outputs the topic title 820 to the answer acquisition unit 350. On the other hand, when the topic title 820 that matches the supplemented first morpheme information is not found, the abbreviated sentence complementing unit 330 passes the first morpheme information and the user input sentence topic specifying information to the topic search unit 340. .

  Next, the topic search unit 340 collates the first morpheme information with the user input sentence topic identification information, and performs a step of searching for the topic title 820 suitable for the first morpheme information from each topic title 820. (Step S1908). Specifically, the topic search unit 340 to which the search command signal is input from the abbreviated sentence complement unit 330 is based on the user input sentence topic identification information and the first morpheme information included in the input search command signal. A topic title 820 suitable for the first morpheme information is searched from the topic titles 820 associated with the user input sentence topic identification information. The topic search unit 340 outputs the topic title 820 obtained as a result of the search to the answer acquisition unit 350 as a search result signal.

Next, the answer acquisition unit 350 determines the utterance type and the topic title 820 of the user determined based on the topic title 820 searched by the topic specifying information search unit 320, the abbreviated sentence complement unit 330, or the topic search unit 340. Collate each associated answer type. The answer acquisition unit 350 that has performed the collation searches for an answer type that matches the determined utterance type from among the answer types (step S1909).
Specifically, the answer acquisition unit 350 to which the search result signal is input from the topic search unit 340 and the “spoken sentence type” is input from the input type determination unit 440 is the “topic title” corresponding to the input search result signal. Based on the entered “spoken sentence type”, the answer type matching the “spoken sentence type” (such as DA) is selected from the answer type group associated with the “topic title”. Identify.

  The answer acquisition unit 350 acquires an answer sentence 830 associated with the specified answer type from the conversation database 500. The answer acquisition unit 350 outputs the acquired answer sentence 830 to the output unit 600 via the management unit 310. The output unit 600 to which the answer sentence is input from the management unit 310 outputs the input answer sentence 830 (step S1910).

[Example of event selection and execution]
Next, an example of event selection and execution by the game system GS according to the present embodiment will be described with reference to FIG. FIG. 20 is a diagram illustrating an example of the number of utterance information and event selection by the game system GS.

  In this example, there are three predetermined periods P1, P2, and P3 from the start of the game to the end of the game, and events corresponding to the respective predetermined periods are selected and executed. In this example, a count value of 1 is added to one utterance (an utterance corresponding to a sentence ending with a punctuation mark). Also, assume that the event table included in the utterance information count unit 800 is set as shown in FIG.

  When the user utters the utterance 1 within the predetermined period P1, the game system GS makes an answer to the utterance 1 and stores the count value from 0 to 1.

  Similarly, from the utterance 2 to the utterance 5 (not shown), the utterance information number counting unit 800 stores the utterance information while incrementing the count value for each utterance. When the utterance 6 is counted, the count value becomes 6. When the count value reaches 6, the predetermined period expires, and the event is selected and executed. The event performed following the predetermined period P1 is “normal event B” corresponding to “5 ≦ utterance information <12” in the event table (FIG. 17B).

  After the end of the event, a new predetermined period P2 starts. The utterance information number counting unit 800 clears the count value 6 in the predetermined period P1, and starts counting the number of new utterance information.

  When the user utters the utterance 7 within the predetermined period P2, the game system GS makes an answer to the utterance 7 and stores the count value from 0 to 1.

  Similarly, from the utterance 8 to the utterance 9 (not shown), the utterance information number counting unit 800 stores the utterance information number while incrementing the count value for each utterance. When the utterance 10 is counted, the count value becomes 3. When the utterance 10 is received, the predetermined period expires, and the game system GS selects and executes an event. As an event that follows the predetermined period P2, a “bad event” corresponding to “utterance information <5” in the event table (FIG. 17B) is executed.

  After the end of the event, a new predetermined period P3 starts. The utterance information number counting unit 800 clears the count value 3 in the predetermined period P2, and starts counting the number of new utterance information.

  When the user utters the utterance 11 within the predetermined period P3, the game system GS makes an answer to the utterance 11 and stores the count value from 0 to 1.

  Similarly, from the utterance 12 to the utterance 26 (not shown), the utterance information number counting section 800 stores the utterance information number while incrementing the count value for each utterance. When the utterance 27 is counted, the count value is 16. The predetermined period expires when the utterance 27 is received, and the game system GS selects and executes an event. The event performed following the predetermined period P3 is a “special event” corresponding to “utterance information ≧ 15” in the event table (FIG. 17B).

  As described above, according to the game system GS, events in the game are controlled based on how many user utterances have been made within a predetermined period.

[Others]
(1) In the above game system, emotion state information handled by the emotion state information management unit 600 may be changed according to the number of utterance information.

For example, it is conceivable to change emotion state information as follows.
(A) When the count value of the utterance information number is greater than or equal to a certain value N, the value of the emotion flag “joy” is increased by N.
(B) When the count value of the utterance information number is greater than or equal to a certain value N, the increase in the values of all emotion flags is increased to M times the normal value.
(C) When the count value of the utterance information number is less than or equal to a certain value N, it is set to 1 / M which is normal for all emotion flags.

With such a configuration, the character provided by the game system GS according to the count value of the number of utterance information can change the response to the user according to the amount of conversation, but can produce an effect. It becomes possible to draw.
(2) The configurations and operations of the speech recognition unit 200, the conversation control unit 300, and the sentence analysis unit 400 of the conversation control device 1 are not limited to those described in the above embodiment, and the user can use the conversation database means. Any speech recognition unit 200, conversation control unit 300, and sentence analysis unit 400 other than those described in the embodiment as long as it returns an answer corresponding to the utterance content, the conversation control according to the present invention. It can be used as a component of the device 1.
(3) In the description of the above embodiment, the input of the utterance to the conversation control device 1 has been described as being by voice. However, the input of the utterance to the conversation control device 1 is not limited to voice, but a keyboard and a touch panel. An utterance is input to the conversation control device 1 as character string data by the non-voice input means 4 such as a pointing device, and the conversation control device 1 answers the utterance input as the character string data using the conversation database 500. The present invention is also established as a configuration for outputting a sentence.

Block diagram showing a configuration example of a game system Block diagram showing a configuration example of a game device Schematic configuration diagram of conversation control device Functional block diagram showing a configuration example of the speech recognition unit Timing chart showing an example of processing of the word hypothesis narrowing unit Flow diagram showing an example of the operation of the voice recognition unit Partial enlarged block diagram of the conversation control device The figure which shows the relationship between the character string and the morpheme extracted from this character string The figure which shows the example of the utterance sentence which corresponds to the type of the utterance sentence, the two letter alphabet which shows the type of the utterance sentence, and the type of the utterance sentence The figure which shows the relationship between the type of sentence and the dictionary for judging the type Conceptual diagram showing an example of the data structure of data stored in the conversation database The figure which shows the correlation with a certain topic specific information and other topic specific information Diagram showing data structure example of topic title Illustration for explaining the type of response The figure which shows the specific example of the topic title matched with a certain topic specific information, an answer sentence, and the amount of conversation Functional block diagram showing a configuration example of the utterance information count unit (A) is an example of an event table for selecting an event according to the number of utterance information, and (B) is an example of an event table different from (A). Flow chart showing the main operations of the game system Flow chart showing an example of conversation control processing Diagram showing examples of utterances, answer sentences, count values, and selected events

Explanation of symbols

GS ... Game system 1 ... Conversation control device 2 ... Game device 3 ... Voice input means 4 ... Non-speech input means 5 ... Image output means 6 ... Voice output means 200 ... Speech recognition unit 300 ... Conversation control unit 400 ... Sentence analysis unit 500 … Conversation database 600… Emotion state information management unit 700… Speech recognition dictionary storage unit 800… Speech information count unit

Claims (3)

In a game machine having a conversation processing unit that outputs an answer sentence in response to an utterance from a user and a game processing unit that executes a game,
The conversation processing unit stores conversation database means for storing a plurality of topic specifying information, a conversation history determined by a previous conversation and a previous answer sentence, and collates topic specifying information determined by the discourse history and utterance information. Conversation control means for selecting an answer sentence, counting means for counting the number of utterance information received within a predetermined period,
Have
The game machine is configured to execute an event associated with the number of utterance information based on the number of utterance information counted by the counting means. 2. The game machine according to claim 1, wherein when the conversation control unit collates the topic specifying information determined by the discourse history and the utterance information and selects any of the topic specifying information, the counting unit A game machine characterized by counting the number of information. The game machine according to claim 1 or 2, further comprising emotion state information management means for managing emotion state information for controlling the emotion of a character provided by the game machine as a user's partner. The information management means receives the number of conversation information obtained as a result of counting from the counting means, and performs predetermined processing of emotion state information according to the number of conversation information.

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