JP4204044B2 - GAME MACHINE, GAME EXECUTION METHOD, AND PROGRAM FOR THEM - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To enable a game machine to have emotional change like a natural human being according to tactics (bluff or deception) set for the game machine. <P>SOLUTION: The game machine comprises a conversation processing part for outputting a sentence of reply in response to the utterance of a user and a game processing part for executing a battle game between the user and a character. The conversation processing part comprises a conversation database means for storing a plurality of topic specifying information, and a conversation control means for storing the history of conversation determined by previous conversations and previous sentences of reply, selecting a sentence of reply according to the emotion state information describing the emotion of the character by collating the topic specifying information determined by the history of conversation and the utterance information, and outputting the information for determining the behavior of the character. The game processing part executes a game based on the information for determining the behavior of the character. <P>COPYRIGHT: (C)2005,JPO&amp;NCIPI

Description

  The present invention relates to a game machine, a game execution method, and a program for the same, and more particularly, when a user makes a so-called bluff when the user can proceed with the game according to the content of the user's utterance. Relates to a game machine in which the game proceeds according to the bluff.

  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 a game based on the conversation, or to have a conversation with a character while playing a 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

  Generally, in a game, apart from the player taking actions necessary for the progress of the game (taking / discarding cards, moving a piece, throwing away a basket, consuming a turn, etc.) Various bargaining is often performed. As a method of bargaining, there is a method of speaking / speaking in addition to changing facial expressions and gestures. When a player has a comment from an opponent, he or she is wondering whether the comment is true or lie, but with reference to the content of the comment, the player tries to take the next action that makes his battle situation even more advantageous. Such bargaining has aspects of psychological warfare that have a considerable influence on the progress, victory and defeat of the game, and it is clear to the public that this game will further increase the fun and thrilling of the game There is a place.

  In a game having a conventional conversation system, an evaluation target for the game machine (CPU) to take the next action in a battle with the game machine (CPU) is mainly an operation selected by the player during the game. In a game having a conventional conversation system, the player responds / replies to the utterance / utterance, but a process that affects the next action taken by the game machine (CPU) according to the player's utterance / utterance is not performed. In other words, the player could not perform bargaining by speaking / speaking in a battle with a game machine (CPU).

  Also, if the player's speech / utterance is a so-called bluff as a result of the bargaining, the opponent will cause emotional changes (anger, surprise, etc.) to this bluff. However, in conventional games, such emotional changes have not been reproduced.

An object of the present invention is to provide a technique that allows a player to perform a game (bluffing, hugging, etc.) on a game machine (CPU) using a conversation function.
Another object of the present invention is to provide a technology capable of causing an emotional change like a natural person in accordance with a game machine (bluff, stagnation, etc.) on which a game (bluff, stagnation) is set. It is in.

As means for solving the above problems, the present invention has the following features.
According to a first aspect of the present invention, a conversation processing unit (conversation control device) that outputs an answer in response to an utterance from a user (player) and a game processing unit (game device) that executes a battle game between the user and a character. It is proposed as a game machine (game system) having

  In this 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 the previous conversation and the previous answer sentence, and a topic specification determined by the conversation history. Conversation control means (conversation) that collates information and utterance information, selects an answer sentence corresponding to emotion state information indicating the character's emotion, and outputs information (corresponding action flag) for determining the character's behavior The game processing unit is characterized in that the game is executed based on information for determining the action of the character.

  According to this game machine, it becomes possible for the user to play with the character by so-called bluffing, and the range of games tactics / strategies can be widened, and the user can play against actual humans. It is possible to give similar fun.

  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. The “match” is not necessarily limited to a one-on-one match format. Even if the game is one-to-many or many-to-many, if the game proceeds in a state of conflicting interests between the user and the character, it is included in the “battle” 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. The meaning of each term is the same in the following second and third aspects.

Further, in this game machine, the emotion state information management means for managing the emotion state information is compared with the user's utterance content input to the conversation processing unit and the user's action command information input to the game processing unit. You may make it further have a user action evaluation means to change emotion state information based on a result.
Here, the “user action command information” refers to information for transmitting the action that the user takes on the game to the game system by means of input means possessed by the game system. For example, in a game system for a mahjong game, It is an instruction to take actions such as selection of trash, Pong, Chi, Kang, Reach, Ron, Tsumo.

  According to such a game machine, when the user wears a so-called bluff, the character can show a change in emotion with respect to the user's speech being a bluff and can realize a more realistic character reaction Become.

In this game machine, the game processing unit may control the display of the character based on the emotional state information.
According to this game machine, it is possible to produce a character that changes facial expressions and actions with respect to the bluff.

The second aspect of the present invention is proposed as a game execution method for executing a battle game between a user and a character.
The game execution method includes an input unit that receives an utterance from the user, a conversation processing unit that outputs an answer sentence in response to the utterance from the user, and a game processing unit that executes a battle game between the user and the character. The game processing unit has a plurality of thinking routines, and has a character action determining unit that determines an action to advance the game taken by the character according to one of these thinking routines, and the conversation processing The section is composed of means for storing a discourse history that is information for specifying a topic or subject of conversation between the user and the conversation processing section, and a morpheme composed of one character, a plurality of character strings, or a combination thereof, and An emotional flag composing emotional state information indicating an answer sentence to the user and an emotion of the character, and a corresponding action flag for designating the thought routine And a conversation database means for storing a plurality of pieces of topic identification information determined by the conversation history, and a method for executing a game on the game machine. A step in which the recognition unit receives and recognizes the utterance from the user accepted by the input means as the utterance information from the user, and the morpheme extraction unit of the conversation processing unit converts the utterance information from the user into one The step of extracting characters, a plurality of character strings, or a morpheme composed of a combination thereof, and the topic specifying information search unit of the conversation processing unit are stored in the conversation database means and the morpheme information extracted by the morpheme extraction unit The topic identification information that matches the topic identification information and matches the morpheme that constitutes the morpheme information from each topic identification information , A step in which an answer acquisition unit of the conversation processing unit acquires an answer sentence associated with the topic specifying information searched by the topic specifying information searching unit, and emotion state information management of the conversation processing unit A step of receiving an emotion flag associated with the answer sentence acquired by the answer acquisition unit, and updating emotion state information based on the emotion flag; and the conversation processing unit Outputting a corresponding action flag associated with the acquired answer sentence to the character action determining unit of the game processing unit, and the character action determining unit based on the corresponding action flag received from the conversation processing unit Selecting a predetermined thought routine, determining a character's action based on the selected thought routine, and a user of the conversation processing unit The behavior evaluation unit receives information indicating the user's behavior, determines whether the user's utterance content matches the user's behavior, and based on the determination result, the emotional state information management unit And a step capable of updating information.

  According to this game execution method, it is possible for the user to play with a character by so-called bluffing, the range of games tactics / strategies can be further widened, and when the user plays against an actual human being It will be possible to give similar fun.

In addition, according to such a game execution method, when the user wears a so-called bluff, the character shows a change in emotion to the fact that the user's utterance is a bluff, and a more realistic character reaction is realized. It becomes possible.

In addition, this invention is materialized also as a program for functioning a computer as a game machine concerning a 1st aspect . The present invention can also be realized as a program for causing a computer to execute the method of the second aspect .

  According to the present invention, when the user's utterance content does not match the action actually taken by the user, such as a so-called bluff, the game machine in which the character reacts to such action, and the game execution method As well as programs for these.

  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 configured to be provided from an external device (for example, an ASP (application service provider server or the like)) and put it in 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 a voice input microphone, for example. 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 has a function of outputting the content output as an answer sentence of the character (pseudo personality) by the conversation control device 1 as a voice signal and outputting voice / sound (sound effect, BGM, etc.) relating 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.
FIG. 3 is a block diagram illustrating a configuration example of the game progress control unit 202.
As shown in the figure, the game progress control unit 202 includes a character action determination unit 202A, a game progress management unit 202B, and a character control unit 202C.

  The character action determination unit 202B receives a thought routine designation command from the conversation control device 1, and in response to the thought routine designation command, an action (for example, a mahjong game) taken by a character played by the game system GS , Choose to throw away the trash from the hand, decide to act on the player's trash, cheer, cheer, cry, declare self, declare reach, etc.) It has the function to do.

The character action determining unit 202A has a plurality of thought routines 202A _{1 to} 202A _N. Thought routines 202A _{1 to} 202A _N are different routines, and determine an action for advancing the game based on different strategies / tactics. For example, the thinking routine 202A ₁ is a thinking routine based on a strategy / tactics that emphasizes handmade speed and does not focus on scoring, and the thinking routine 202A ₂ focuses on making a high-scoring role and focusing on listening speed. Thinking routine based on strategy / tactics not to be considered ... The thinking routine 202A _N is a thinking routine based on strategy / tactics that emphasizes protection and does not transfer money even if the hearing is abandoned.

The action for progressing the game is determined by any _{one of the} plurality of thought routines 202A _{1 to} 202A _N. Note that which thinking routine is selected is determined by a thinking routine designation command issued by the conversation control device 1 according to the utterance content of the user (player).

  Moreover, the character action determination unit 202A can determine which thinking routine to use based on the situation (the score, the remaining time until the game ends) regarding the game without using the thinking routine designation command.

  The game progress management unit 202B has a function of managing the progress of the game. For example, taking a mahjong game as an example, the game progress management unit 202B starts the game, such as the arrangement of the kites to be stacked on the yamaha, the determination of the layout, the grasp of the user and the character's manual, the judgment of the rise, and the calculation of the score. All of the management from the end of the game to the end of the game, decide what kind of image to display and what kind of sound to output according to the state of the game being managed, image processing unit 203, sound processing The unit 204 is instructed to output the determined image and sound.

  The character control unit 202C refers to emotion state information indicating the character's emotion, determines what character image to display, and notifies the game progress management unit 202B. For example, when the emotion state information indicates “joy”, the character control unit 202C notifies the game progress management unit 202B to display a smiling character image.

Returning to FIG. 2, the description of the configuration example of the game apparatus 2 is continued.
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.

[Configuration example of conversation control device]
Next, a configuration example of the conversation control device 1 that is a component of the game system GS will be described.
[overall structure]
FIG. 4 is a functional block diagram showing a schematic configuration of the conversation control apparatus 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, a speech recognition dictionary storage unit 600, and an emotional state information management unit 700. And a user behavior evaluation 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 600 to obtain the voice signal. The speech recognition result estimated from the above is output. In the configuration example illustrated in FIG. 4, 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. 5 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 600, and the candidate determination unit 200E is connected to the conversation control unit 300.

  The speech recognition dictionary storage unit 600 connected to the word matching unit 200C stores a phoneme hidden Markov model (hereinafter, the hidden Markov model is referred to as an HMM). 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. The speech recognition dictionary storage unit 600 connected to the word collation 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, and based on the feature parameter data input via the buffer memory 200B, the phoneme HMM and the word dictionary stored in the speech recognition dictionary storage unit 600 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. Further, 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 600 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. 6 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 600 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. And for example 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 600, 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. 7 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 S701). Next, this feature parameter is compared with the phoneme HMM and language model stored in the speech recognition dictionary storage unit 600 to obtain a predetermined number of word hypotheses and their likelihoods (step S702). 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 S703 and S704). If there is a match, the speech recognition unit 200 outputs the matching word hypothesis as a recognition result (step S705). 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 S706).

[Voice recognition dictionary storage]
Returning to FIG. 4 again, the description of the configuration example of the conversation control device 1 is continued.
The speech recognition dictionary storage unit 600 stores a character string corresponding to a standard speech 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. 4 and 8. FIG. 8 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. 8, 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 the present embodiment, as shown in FIG. 8, 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. 9 is a diagram illustrating a relationship between a character string and a morpheme extracted from the character string. As shown in FIG. 9, each morpheme can be expressed as m1, m2, m3... In the present embodiment. As shown in FIG. 9, 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, it does not hinder the realization 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. 10 is a diagram illustrating an example of “spoken sentence type”, a two-letter alphabet representing the type of the spoken sentence, and a speech sentence corresponding to the type of the spoken sentence.

  In this embodiment, as shown in FIG. 10, “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. 11 shows a dictionary to be used and types of determinations to be 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. 12 is a conceptual diagram illustrating a configuration example of data stored in the conversation database 500.

[Topic specific information]
As shown in FIG. 12, the conversation database 500 stores in advance a plurality of topic specifying information (810) A to F for specifying a topic. Each of the topic identification information (810) A to F may be associated with other topic identification information. For example, in the example shown in FIG. 12, when the topic identification information (810) C is identified, Other topic specifying information (810) A, B, D associated with this topic specifying information (810) C 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.

  An emotional condition parameter 840 is associated with the answer sentence 830. The emotion condition parameter 840 is information indicating a condition of emotion state information. For example, when the emotion state information indicates the cumulative value “10” of the emotion flag “anger”, the emotion condition parameter 840 of a certain response sentence A describes the cumulative value 5 or less of the emotion flag “anger”. When the emotion condition parameter 840 of the other answer sentence B describes the cumulative value 8 or more of the emotion flag of “anger”, the answer sentence A is not selected and the answer sentence B is the user of the conversation control device 1. Will be selected as an answer to.

  Furthermore, an emotion flag 850 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 850 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 “furious” emotion is indicated. The character data “B” is assigned as the flag 850, the character data “C” is assigned as the emotion flag indicating the feeling of “anger”, the character data “D” is assigned as the emotion flag indicating the feeling of “joy”, and so on. To do. The conversation control device 1 according to the present embodiment refers to the emotion flag 850 and controls the emotion of the pseudo personality provided by the conversation control device 1.

  Further, in the answer sentence 830, a corresponding action flag 860 for designating a thought routine for the game apparatus 1 to determine the action of the character is stored in association with it. The corresponding action flag 860 may be any data as long as it can specify a thought routine, and is, for example, a thought routine number or ID.

Next, the association between certain topic specifying information and other topic specifying information will be described. FIG. 13 is a diagram showing the 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. 13, 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 the 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 conversation control apparatus 1 according to the present embodiment identifies certain topic identification information 810 by referring to the stored content of conversation database 500, other topic identification information stored in association with topic identification information 810 is stored. 810 and the topic title 820, the answer sentence 830, the emotion right parameter 840 associated with the answer sentence 830, the emotion flag 850, the corresponding action flag 860, etc. Is possible.

[Topic title]
Next, a data configuration example of the topic title 820 (also referred to as “second morpheme information”) will be described with reference to FIG. FIG. 14 is a diagram illustrating a data configuration example of the topic title 820.

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. 14, each topic title 820 is information configured by 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. 14, 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. 15, 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]
Next, returning to FIG. 8, a configuration example of the conversation control unit 300 will be described.
The conversation control unit 300 includes each component in the conversation control device 1 (speech recognition unit 200, sentence analysis unit 400, conversation database 500, emotion state information management unit 700, speech recognition dictionary storage unit 600, emotion state information management unit 700, User behavior evaluation unit 800) has a function of controlling data exchange between the user behavior evaluation unit 800), determining and outputting an answer sentence responding to an utterance, and outputting a thought routine designation command.

  In the present embodiment, the conversation control unit 300 includes, as shown in FIG. 8, a management unit 310, a topic identification 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 has a function of passing the corresponding action flag 860 to the user action evaluation unit 800. .

  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 820 that matches the first morpheme information after completion based on the set “D”. When a topic title 820 that matches the first morpheme information after complement is found, the abbreviated sentence complement unit 330 outputs the topic title 820 to the answer acquisition unit 350. The answer acquisition unit 350 can output the answer sentence 830 most suitable for the user's utterance content based on the appropriate topic title 820 searched by the abbreviated 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 included in any of the first specific information, the second specific information, or the third specific information constituting the topic title 820 based on the topic title of interest. It may be included in the morpheme 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. 16 is a diagram illustrating specific examples of the topic title 820, the answer sentence 830, the emotion condition parameter 840, the emotion flag 850, and the corresponding action flag 860 associated with certain topic identification information 810 (= “Sato”). is there. As illustrated in FIG. 16, for example, the topic search unit 340 includes the topic specifying information 810 (= “Sato”) in the input first morpheme information “Sato, I like”, so the topic specifying 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”.

  FIG. 17 shows another topic specifying information 810 (= “cannot be raised”), topic title 820, answer sentence 830, emotion condition parameter 840, emotion flag 850, and corresponding action flag 860. The conversation control unit 300 selects the topic specifying information 810 (= “cannot be raised”) and the topic title (820) 1-1 ((I; *; cannot be raised), and the type of the utterance is “DA”. Then, the conversation control unit 300 selects an answer sentence 830 that says, “It ’s bearish. Do n’t take care of it” or “Would you like to attack at once?” According to the emotional state information stored at that time, “A” is output to the emotional state information management unit 700 as the emotion flag 850, and a thinking routine designation command for designating “thinking routine 2” as the corresponding action flag 860 is output to the game apparatus 2.

Returning to FIG. 8, the description of the configuration example of the conversation control unit 300 will be continued.
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]
Based on the topic title 820 searched by the topic search unit 340, the answer acquisition unit 350 acquires an answer sentence 830, an emotion condition parameter 840, an emotion flag 850, and a corresponding action flag 860 associated with the topic title 820. To do. Further, the answer acquisition unit 350 determines each answer type associated with the topic title 820 and the utterance type determined by the input type determination unit 440 based on the topic title 820 searched by the topic search unit 340. Collate. 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. 16, for example, when the topic title 820 searched by the topic search unit 340 is the topic title 1-1 (Sato; *; likes), the topic title 1- The answer type (for example, DA) determined by the input type determination unit 440 from the answer text (830) 1-1 (DA, TA, etc.) associated with 1 DA) is specified. 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 830 associated with the affirmative form (A) includes, for example, “isn't it a hobby to play with pachinko?” (Question sentence (Q) to ask 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 850 corresponding to the answer sentence 830 to the emotion state information management unit 700.
Furthermore, the answer acquisition unit 350 outputs the acquired corresponding action flag 860 to the game device 2 (game progress control unit 202). This makes it possible to specify a thought routine so as to take an action related to the progress of the character game in accordance with the user's utterance.
Above, description of the conversation control part 300 is complete | finished.

[Emotion State Information Management Department]
Returning to FIG. 4, the emotional state information management unit 700 of the conversation control device 1 will be described.

  The emotional state information management unit 700 has a function of receiving the emotional flag 850 from the conversation control unit 300 and updating the emotional state information based on the emotional flag 850. When emotion state information management unit 700 receives emotion flag 850 from conversation control unit 300, emotion state information management unit 700 stores the emotion flag 850, 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.

  When emotion state information management unit 700 receives emotion flag 850, emotion state information management unit 700 rewrites emotion state information so that the count value of emotion flag 850 is changed by a predetermined value (for example, +1). When emotion state information management unit 700 receives user behavior evaluation information from user behavior evaluation unit 800 described later, emotion state information management unit 700 rewrites emotion state information with a predetermined content (for example, count value of emotion flag 850 of “anger” is set to 10). Or the count value of each emotion flag 850 of “sadness” or “anger” is set to double the current value).

  FIG. 18 shows a data configuration example of emotional state information stored in the emotional state information storage unit 7B. The emotion state information 1800 has a cumulative value 1802 for each emotion flag type 1801. The cumulative value 1802 can be increased or decreased for each emotion flag. For example, every time the emotion state information management unit 700 receives an emotion flag indicating “joy”, the value of the cumulative value 1802 is incremented. Also, the cumulative value 1802 is increased or decreased according to the user behavior evaluation information from the user behavior evaluation unit 800. The emotion state information management unit 700 stores in advance as a program how to increase / decrease, and a predetermined increase / decrease process is executed according to this program.

[User Behavior Evaluation Department]
Next, the user behavior evaluation unit 800 will be described. The user behavior evaluation unit 800 has a function of determining whether the user's utterance content matches the user's behavior and updating the emotional state information in accordance with the determination result.

  In the present embodiment, the user behavior evaluation unit 800 receives the corresponding behavior flag 860 from the conversation control unit 300 and then receives a notification of the user's behavior (for example, throwing away, pong, ron, tsumo) from the game apparatus 2. Based on this action and the corresponding action flag 860, user action evaluation information is generated and passed to the emotional state information management unit 700.

  For example, it is assumed that the user makes an utterance “not yet listened” and the corresponding action flag 860 is selected that specifies a thought routine aiming at making a high role according to the utterance. The user action evaluation unit 800 has a user action evaluation table associated with the corresponding action flag 860, and this table corresponds to the corresponding action flag 860 for designating a thought routine aiming at high role, It does not go up within "is described. The user behavior evaluation unit 800 monitors whether the user behavior does not violate this condition.

  Here, when the user violates this condition and declares agari within three hands from the utterance, the user behavior evaluation unit 800 sends the emotion state information to the emotion state information management unit 700 and sends the emotion state information to the character user. User behavior evaluation information is sent to request an update that reduces density and reliability. The emotional state information management unit 700 updates the emotional state information in accordance with the user behavior evaluation information, and the character's answer and character image (facial expression, action) are changed to the emotional state information whose intimacy and reliability are reduced. Will be made based on. By the operation of the user behavior evaluation unit 800 as described above, when viewed from the user, the character is caught in the bluff worn by the user, and an effect is made so that the character looks offended.

On the other hand, if the user does not violate this condition and does not declare agari within 3 moves from the utterance, the user behavior indicating that the emotional state information is requested to be updated to increase the familiarity and reliability of the character user. You may make it send evaluation information.
This is the end of the description of the game system GS.

[Game system operation example]
Next, an operation example of the game system GS will be described.
FIG. 19 is a flowchart showing main processing 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 S1901).

  Next, the conversation control device 1 performs conversation control processing for returning an answer sentence 830 in response to the received user utterance (step S1902).

[Conversation control method]
The conversation control process (step S1902) by the conversation control apparatus 1 having the above-described configuration can be performed according to the procedure shown in FIG. FIG. 20 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 S2001). 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 S2002). 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.

  And the character string specific | specification part 410 performs the step which divides | segments a series of character strings specified by the speech recognition part 200 for every sentence (step S2003). 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 S2004). 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 S2005). 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 compares the first morpheme information extracted by the morpheme extraction unit 420 with the topic topic title 820focus (step S2006).
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 S2007). 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 S2008). 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 the answer type that matches the determined utterance type from among the answer types (step S2009).

  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 response acquisition unit 350 corresponds to the response text 830 to the management section 310, the emotion flag 850 corresponding to the response text 830 to the emotion status information management section 700, and the response text 830 via the management section 310. The corresponding action flag 860 is output to the game apparatus 2 respectively. (Step S2010).
Thus, the conversation control process (step S1902) ends.

Returning to FIG. 19, the description of the operation example of the game system GS is continued.
Continuing to step S1902, the conversation control device 1 performs emotion state information update processing based on the emotion flag 850 corresponding to the answer sentence 830 determined in the conversation control processing (step S1903). When the answer sentence 830 is determined, the conversation control unit 300 passes the emotion flag 850 corresponding to the answer sentence 830 to the emotion state information management unit 700. The emotional state information management unit 700 performs a predetermined process to rewrite the emotional state information so that the emotional flag 850 is reflected in the emotional state information.

  Next, the conversation control apparatus 1 passes the corresponding action flag 860 corresponding to the answer sentence 830 determined in the conversation control process to the game apparatus 2 and causes the game apparatus 2 to perform a thinking routine selection process (step S1904). When the answer sentence 830 is determined, the conversation control unit 300 passes a corresponding action flag 860 corresponding to the answer sentence 830 to the game apparatus 2.

  The game apparatus 2 selects a thinking routine designated by the corresponding action flag 860 by the character action determining unit 202A, determines an action of the character based on the thinking routine, and performs an action determining process to be executed (step S1905). ). For example, in a mahjong game, the character to be cut next is determined by the selected thinking routine, and the game proceeds with the character throwing away that character.

  Subsequently, when the game system GS receives a user action, the game system GS performs a user action evaluation process that compares and evaluates the action and the content of the previous utterance (step S1906). FIG. 21 shows a processing example of the user behavior evaluation processing. First, the game system GS performs a user behavior accepting process for receiving a command indicating a user behavior from the voice input means 3 or the non-voice input means 4 (step S2101). The command indicating the user's action here is selection and determination of abandonment, Pon, Chi, Kang, Ron, Reach, etc. in the mahjong game.

  When receiving the instruction indicating the user's action, the game system GS compares the content of the previous utterance with the user action (step S2102).

  If there is no contradiction as a result of comparing the previous utterance content with this user action (step S2103, NO), the user high evaluation process (step S1906) is terminated as it is. On the other hand, if there is a contradiction between the two (step S2103, YES), the user behavior evaluation unit 800 passes an emotion state information change command to the emotion state information management unit 700 (step S2104).

Returning to FIG. 19, the description of the operation example of the game system GS is continued.
When the emotional state information management unit 700 receives the emotional state information change command, the emotional state information management unit 700 executes emotional state information change processing (step S1907). This emotional state information change process uses the emotional state information to change the emotional state of the character (surprise, angry, gentleness, etc. for being bluffed) in response to the user taking an action that does not follow the user's previous statement. It is a process to reflect. The remarks after the emotion state information change process are in response to the emotional change of the character, which is useful for realizing a more natural character response.

  Subsequently, the game system GS determines whether or not the game end condition is satisfied (step S1908). 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 S1901 and waits for acceptance of the user utterance. Thereafter, the game progresses while steps S1901 to S1907 are repeated until the game end condition is satisfied.

  In addition, following the user behavior evaluation process in step S1906, the user behavior evaluation unit 800, when there is a contradiction between the two (step S2103, YES), causes the conversation control unit 300 to take a behavior that does not follow the user's previous remarks. It may be configured to pass predetermined topic specifying information 810 and topic title 820 so that a character's answer sentence 830 for taking out is taken. As an example of the answer sentence 830 in this case, a sentence indicating dissatisfaction or surprise to the user's utterance content being bluff may be set.

[Specific example of operation of game system GS]
Next, a specific example of the operation of the game system GS according to the present embodiment will be described with reference to FIG. FIG. 22 is a sequence diagram showing a specific example of the operation of the game system GS.

  This game system GS performs a battle-type mahjong game, and is in a state where the game is progressing and is in the final game. Here, while listening, the user tries to bluff the character, and the voice input means 3 notifies the game system GS of an utterance “I'm not going to go up yet” (step S2201).

  In response to this utterance, the game system GS performs a conversation control process for returning an answer sentence 830 (step S2202), and updates emotion state information based on the emotion flag 850 corresponding to the conversation sentence (step S2202). S2203).

  Further, the game system GS performs a thought routine selection process based on the corresponding action flag 860 associated with the answer sentence 830 selected in the conversation control process (step S2202) (step S2204). In this example, it is estimated that the user's handmade will still take until hearing, and a handmade thought routine that gives a high score is selected. Further, based on the selected thinking routine (or corresponding action flag), the user action evaluation unit 800 sets an evaluation condition for user action. In this example, it is assumed that the condition that “the user does not rise within three rounds from now” is set as the evaluation condition of the user action.

  The game system GS outputs, as the answer sentence 830 selected in the conversation control process, “Fufufu, let's attack at once” (step S2205), and determines the character action by the above thinking routine (step S2205). S2206). In this example, the character played by the game system GS does not consider the speed until listening, selects the discarded cocoon so as to obtain a high score, and performs a process of producing a cocoon on the river (Ho).

  The trap thrown away by this character was the hit by the user. The user inputs the utterance “Ron!”, Which is a declaration, to the game system GS through the voice input means 3 (step S2207).

  For this user action “Ron”, the game system GS performs a user action evaluation process (step S2208). The user behavior evaluation unit 800 determines that the behavior “Ron” of the user's behavior user violates the above-described user behavior evaluation condition “the user will not rise within three rounds from now”, and the emotional state information management unit 700 To change the emotion state information so that the emotion of the character with respect to the user deteriorates. The emotion state information management unit 700 changes the emotion state information in response to this request. For example, it is conceivable that the emotional state information is changed so that the character is furious with respect to the user, the reliability with respect to the user is decreased, or the intimacy with respect to the user is decreased. How to change the emotional state information may be appropriately determined according to the game setting, the character personality setting, the cumulative number of bluffs performed by the character, and the like.

  In addition, the game system GS outputs an answer sentence 830 in response to the user utterance “Ron!” (Step S2210). Although the answer sentence 830 explicitly notifies the user that the emotion of the character has been changed, it does not necessarily have to be explicitly communicated. Character image display control based on the emotional state information changed in step S2209 in accordance with the answer sentence 830 (such as displaying a character with an angry facial expression or displaying a moving image showing the character's upset state). May be performed.

  The conversation between the user and the character in the subsequent game is performed based on the emotional state information changed in step S2209, and the user receives a response from the character whose emotion has been changed with respect to the bluff, and exhibits a more natural reaction. You will be able to enjoy the character performance.

[Other variations]
(1) The user behavior evaluation unit 800 counts the number of times that the user has bluffed, and changes the special emotional state information (for example, what reaction is furious when this number exceeds the predetermined number). Or the like may be performed, or the change of the thinking routine based on the utterance is stopped for the user.

Block diagram showing a configuration example of a game system Block diagram showing a configuration example of a game device Block diagram showing a configuration example of the game progress control unit Block diagram showing a configuration example of a 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, an emotion state parameter, an emotion flag, and a corresponding action flag The figure which shows the specific example of the topic title matched with another topic specific information, an answer sentence, an emotional state parameter, an emotion flag, and a corresponding action flag The figure which shows the data structural example of emotion state information Flow chart showing the main operations of the game system Flow chart showing an example of conversation control processing Flow chart showing an example of user behavior evaluation processing Sequence diagram showing an operation example of the game system

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… Speech recognition dictionary storage unit 700… Emotion state information management unit 800… User behavior evaluation unit

Claims (9)

In a game machine having input means for receiving an utterance from a user, a conversation processing unit that outputs an answer sentence in response to the utterance from the user, and a game processing unit that executes a battle game between the user and a character,
The game processing unit
Having a plurality of thinking routines, and having a character action determining unit for determining an action to advance the game taken by the character according to one of these thinking routines;
The conversation processing unit
A recognition unit that receives and recognizes the utterance from the user accepted by the input means as the utterance information from the user;
Means for storing a discourse history that is information for identifying a topic or subject of conversation between the user and the conversation processing unit;
An emotion flag that is composed of a morpheme made up of one character, a plurality of character strings, or a combination thereof, and that constitutes an emotional state information indicating an answer sentence to the user and the emotion of the character, and a corresponding action flag for designating the thought routine A conversation database means for storing a plurality of topic identification information determined by the discourse history and a topic title associated with
A morpheme extraction unit that extracts speech information from the user into a morpheme composed of one character, a plurality of character strings, or a combination thereof;
Collating the morpheme information extracted by the morpheme extraction unit with the topic specifying information stored in the conversation database means, and selecting the topic specifying information that matches the morpheme constituting the morpheme information from each topic specifying information Means for selecting the answer sentence to the user by:
An emotion state information management unit that receives an emotion flag associated with the selected answer sentence and updates the emotion state information based on the emotion flag;
Means for outputting a corresponding action flag associated with the selected answer sentence to the character action determining section in order to specify a thought routine involved in the action determination of the character action determining section;
In addition to receiving information indicating the user's behavior, it is determined whether the user's utterance content matches the user's behavior, and the emotional state information management unit updates the emotional state information based on the determination result A user behavior evaluation unit capable of
Game machine, characterized in that it comprises a. The game machine according to claim 1 , wherein the game processing unit controls display of a character based on the emotion state information . Along with recognizing speech information as a series of character strings, a character string specifying unit that separates the character strings into one sentence,
An input type determining unit that determines the type of utterance content based on the character string specified by the character string specifying unit, and outputs information indicating the utterance type to the means for selecting the answer sentence according to the determination result;
The game machine according to claim 1, further comprising: It further comprises an abbreviated sentence complementing unit that generates a plurality of types of complemented morpheme information by complementing the morpheme information using the topic identification information included in the previous topic identification information and the previous answer sentence. The game machine according to any one of claims 1 to 3 . An input means for receiving an utterance from a user, comprising: a conversation process unit to output a reply sentence in response to the utterance from a user, and a game processing unit for executing the competition game between the user and the character, the game processing unit And a character action determining unit that determines an action for proceeding with the game taken by the character according to one of these thinking routines, and the conversation processing unit performs conversation processing with the user. An answer sentence to a user and a character composed of means for storing a discourse history that is information for specifying a topic or subject of conversation with a department, and a morpheme composed of one character, a plurality of character strings, or a combination thereof. A topic formed by associating an emotion flag constituting emotion state information indicating an emotion of a person and a corresponding action flag for designating the thought routine A method for executing a game in the game machine comprising and a conversation database means title storing a plurality of topic specifying information determined by the conversation history with associated is,
A step in which the recognition unit of the conversation processing unit receives and recognizes an utterance from a user accepted by the input means as utterance information from the user;
The morpheme extraction unit of the conversation processing unit extracts the utterance information from the user into a morpheme consisting of one character, a plurality of character strings, or a combination thereof;
The topic identification information search unit of the conversation processing unit collates the morpheme information extracted by the morpheme extraction unit with the topic identification information stored in the conversation database unit, and constructs morpheme information from each topic identification information Searching for topic specific information that matches a morpheme to
A step in which an answer acquisition unit of the conversation processing unit acquires an answer sentence associated with the topic identification information searched by the topic identification information search unit;
The emotion state information management unit of the conversation processing unit receives an emotion flag associated with the answer sentence acquired by the answer acquisition unit, and updates the emotion state information based on the emotion flag;
The conversation processing unit outputting a corresponding action flag associated with the answer sentence acquired by the answer acquiring unit to the character action determining unit of the game processing unit;
The character action determining unit selecting a predetermined thinking routine based on the corresponding action flag received from the conversation processing unit, and determining a character action based on the selected thinking routine;
The user behavior evaluation unit of the conversation processing unit receives information indicating the user's behavior, determines whether the user's utterance content matches the user's behavior, and emotion state information based on the determination result A step capable of causing the management unit to update the emotional state information;
A method comprising: After the step in which the morpheme extraction unit of the conversation processing unit extracts the utterance information from the user into the morpheme, the character string specifying unit of the conversation processing unit recognizes the utterance information as a series of character strings, and the character string A step for separating each sentence into phrases,
The input type determination unit of the conversation processing unit determines the type of utterance content based on the character string specified by the character string specifying unit, and outputs information indicating the utterance type to the answer acquisition unit according to the determination result And steps to
The method of claim 5 further comprising: After the step in which the topic specifying information search unit of the conversation processing unit searches for topic specifying information, the abbreviated sentence complementing unit of the conversation processing unit determines the topic specifying information included in the previous topic specifying information and the previous answer sentence. The method according to claim 5, further comprising generating a plurality of types of complemented morpheme information by using the complement of the morpheme information. The program for functioning a computer as a game machine of any one of Claim 1 thru | or 4 . The program for making a computer perform the method of any one of Claim 5 thru | or 7 .

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