JP4204041B2 - Game machine, game execution method, and program - Google Patents

Description

  The present invention relates to a game machine, a game execution method, and a program that allow a user to play a game while having a conversation with a character having a virtual personality, and more specifically, information related to a game in a storage medium such as an IC card. And a game execution method and a program that can be reflected in the emotion of the character based on this information.

  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

  In the game incorporating the conventional conversation system as described above, the dialogue with the character is determined by the content of the user's utterance. That is, the answer to the user's utterance refers to the contents of a database that stores a plurality of predetermined answers, searches the database for an answer corresponding to the utterance, and returns it to the user. In such a method of simply searching for an answer from the database, the character's emotional change with respect to the user's utterance and the character's emotional change according to the game win / loss cannot be reflected in the answer.

  For example, in the method of controlling the conversation based only on the database as described above, the reaction of the character at the start of the game has a certain content, and the number of times the user has played the game before, the frequency, and the continuous game performance The character will respond and answer in the same way regardless of the battle record, and there is a problem that nature closer to humans does not respond and lacks interest.

  An object of the present invention is to provide a game machine capable of performing conversation control reflecting the game situation of a conventional game.

As means for solving the above problems, the present invention has the following features.
The first aspect of the present invention is proposed as a game machine (game system). In order to respond to the user's utterance information, the game machine refers to the conversation history determined by the previous conversation and the previous answer sentence, collates the topic identification information determined by the discourse history and the utterance information, and determines the answer sentence. Connected to the output conversation processing means (conversation control device), the game control means (game device) for controlling the character and executing the game, the emotion state information management means, the game control means and the emotion state information management means The game history information storage means, and the conversation processing means outputs an answer sentence with reference to the emotion state information stored by the user's utterance and emotion state information management means, and an emotion variable (emotion flag). The game control means controls the character with reference to the emotion state information, and controls the game state related to the game. Information is output, the game history information storage means stores game history information including emotional state information and game state information, and the conversation processing means refers to the game history information to select an answer sentence. .

  According to the game machine concerning this aspect, it becomes possible to provide the character reply reflecting the play of the conventional game.

  In the game machine according to the first aspect, the game state information storage unit may be a storage medium that is removable from the game machine. According to this configuration, the user can carry game history information indicating the play of the previous game, and by referring the game history information to the game machine at the next game play, Without being limited to the execution place, it is possible to provide a conversation with the character according to the previous game state.

  In the game machine according to the first aspect, when the game is started, an answer sentence may be selected with reference to the game state information. According to such a configuration, it is possible to provide the character's answer as if the character had memorized the previous game at the start of the game.

  Furthermore, in the game machine according to the first aspect, the emotion state information may be information indicating a relationship between the user and the character played by the game machine. According to such a configuration, it becomes possible to take over the emotional state (angry, laughing, joyful, etc.) of the previous character to the next game, and continue playing the game with the same character. It is possible to make the user feel as if he is.

The second aspect of the present invention is proposed as a game execution method in which a game is executed while a user and a character have a conversation.
In order to respond to the user's utterance information, this method refers to the conversation history determined by the previous conversation and the previous answer sentence, collates the topic identification information determined by the conversation history with the utterance information, and determines the emotion of the character. A step of selecting and outputting an answer sentence with reference to the emotional state information to be displayed, a step of outputting game state information that is information relating to the game, and an emotion variable is determined in accordance with the selection of the answer sentence. The method includes a step of reflecting information, a step of storing game history state information including game state information and emotion state information, and a step of selecting an answer sentence with reference to the game history state information.

  According to the method according to this aspect, it is possible to cause the character to make an answer reflecting the play of the previous game.

  In this method, the game state information may be stored in a storage medium portable to the user. According to such a configuration, the user can carry game history information indicating the play of the previous game, and by referring to the game history information at the next game play, the user can change the game machine or execute the game. Without being limited to the place, it is possible to provide a conversation with the character according to the previous game state.

  In the above method, the step of selecting an answer sentence may be performed when the game is started. According to such a configuration, it is possible to provide the character's answer as if the character had memorized the previous game at the start of the game.

  Furthermore, in the above method, the emotional state information may be information indicating the relationship between the user and the character played by the game machine. According to such a configuration, it becomes possible to take over the emotional state (angry, laughing, joyful, etc.) of the previous character to the next game, and continue playing the game with the same character. It is possible to make the user feel as if he is.

  The above aspect of the present invention is also realized as a program for realizing a computer function as the game machine or a program for causing a computer to execute the method.

  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.

  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.

  When a game is started, a more natural human-machine conversation that reflects the previous game state is established by referring to the game history information indicating the previous game play state. be able to.

  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, an image output unit 5 connected to the conversation control device 1 and the game device 2, and a conversation. It has emotion state information management means 7 connected to the control device 1 and the game device 2, and a game history information storage means 8 connected to the conversation control device 1, the game device 2, and the emotion state information management means 7. Yes.

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

  In addition, the conversation control device 1 has a function in which the game system GS outputs a reply sentence corresponding to an emotion as a character such as a user's opponent.

  The emotion of the character played by the conversation control device 1 is described by emotion state information. The emotional state information may be any information as long as information indicating emotions can be stored cumulatively. For example, a cumulative value of emotional flags (emotion flag count value) can be used as emotional state information.

  As long as the emotion flag is information that can distinguish emotions, any data may be used. For example, the character data “A” is assigned as an emotion flag indicating “normal” emotion, and the emotion flag indicating “furious” emotion is indicated. Character data “B” is assigned, character data “C” is assigned as an emotion flag indicating “anger”, character data “D” is assigned as an emotion flag indicating “joy”, and so on. The conversation control device 1 according to the present embodiment controls the emotion of the character (pseudo personality, virtual personality) provided by the conversation control device 1 with reference to the emotion state information constituted by the emotion flag. .

  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. In addition, the game apparatus 2 performs character display control so that the display of the character is changed (for example, a smile, an angry face, a crying face, etc.) in accordance with a change in the character's emotion played by the conversation control apparatus 1.

  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 storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, a CD (Compact Disc), a DVD (Digital Video Disc), etc. Alternatively, it may be provided from an external device (for example, a server of an ASP (Application Service Provider)), and this may be placed on the main memory.

  In FIG. 1, the conversation control device 1 and the game device 2 are displayed as independent devices, but the conversation control device 1 and the game device 2 do not necessarily have to be independent devices. A device (CPU), a main memory (RAM), a read-only memory (ROM), an input / output device (I / O), an external storage device such as a hard disk device are commonly used, and the conversation control device 1 is connected to the conversation control device 1 by the same hardware. The game device 2 may be configured to be realized.

  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.

  The emotion state information management means 7 receives the emotion flag output from the conversation control device 2, reflects the emotion flag in the emotion state information, returns the current emotion state information to the conversation control device 2, and responds to the emotion state information. Output the answer text.

  In the configuration example shown in FIG. 1, the emotional state information management unit 7 is displayed as a component independent of the conversation control device 1 and the game device 2, but the emotional state information management unit 7 may be the conversation control device 1 or the game device. 2 may be installed.

  The game history information storage unit 8 has a function of storing game history information and providing the game history information to the conversation control device 1. The “game history information” is information collected and accumulated by playing a game by the same user, and includes emotional state information and information about a previously played game. The “information about the game played before” includes at least one of the following information.

(1) Game play frequency (number of games within a predetermined period)
(2) Total number of games played (3) Game results (average score, highest score, lowest score, etc.), results (wins, losses, win rate)
(4) Last play date and time (5) Play time

  The game history information storage means 8 may be anything as long as it can store game history information and can hold it readable, for example, an IC card, a magnetic card, a semiconductor memory, a PDA (Personal Data Assistant), mobile phone, etc.

  The game history information storage unit 8 may be a storage device or a storage medium built in the game system GS, or may be a storage medium separable from the game system GS. In the case where the game history information storage means 8 is a storage medium that can be separated from the game system GS, the user can enjoy the game on the various types of game systems GS while carrying the game history information storage means 8 to a game enemy. It becomes like this. For example, when a user plays a game at a certain game center, stores the game history information in the brought game history information storage means 8, and later plays the game at another game center, the game history information storage means The game history information stored in FIG. 8 can be read into the game system GS, and the game can be played with the previous game state taken over.

  When the game history information storage means 8 is a storage medium separable from the game system GS, the game history information can be transmitted and received between the game system GS and the game history information storage means 8 by any data transmission method. Absent. For example, mechanical connection (PC card and PC slot, etc.), infrared communication, data writing / data reading by non-contact IC card, game history information is converted into image data such as barcode, and this is displayed and optically displayed. Data transmission methods such as a method of reading from a reading device and a short-range wireless communication method can be used.

[Game system components]
[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, are not related to the progress inside game of the user's utterance ( "Hello", "name?") For the string / instruction conversion unit 201 is not the any of the output.

  The game progress control unit 202 has a function of causing the game to proceed in accordance with user input and designating data for screen display and data for audio output according to the progress.

The game progress control unit 202 receives the emotional state information from the emotional state information management unit 7, and executes image control of the character in response thereto. For example, if the emotional state information indicates a state in which the character is pleased, the character is controlled so as to show a happy expression or action.
The game progress control unit 202 has a function of passing a part of the game history information to the game history information storage unit 8 and storing it.

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 203 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 the voice data. Provided to means 6.

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

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

[Configuration example of voice recognition unit]
FIG. 4 is a functional block diagram illustrating a configuration example of the voice recognition unit 200. The speech recognition unit 200 includes a feature extraction unit 200A, a buffer memory (BM) 200B, a word matching unit 200C, a buffer memory (BM) 200D, a candidate determination unit 200E, and a word hypothesis narrowing unit 200F. Yes. The word matching unit 200C and the word hypothesis narrowing unit 200F are connected to the speech recognition dictionary storage unit 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 is related to “movie”, and its 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 300. 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. 5 is a timing chart showing an example of processing of the word hypothesis narrowing-down unit 200F.
For example, when the i-th word Wi consisting of the phoneme sequence a1, a2,..., An comes after the (i-1) -th word Wi-1, six hypotheses Wa, 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 a computer such as a digital electronic computer, for example, and include a 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, 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. 6 is a flowchart showing an operation example of the speech recognition unit 200. When receiving a voice signal from the voice input unit 3, the voice recognition unit 200 performs a feature analysis of the input voice and generates a feature parameter (step S601). Next, this feature parameter is compared with the phoneme HMM and language model stored in the speech recognition dictionary storage unit 600 to obtain a predetermined number of word hypotheses and their likelihoods (step S602). Next, the speech recognition unit 200 compares the acquired predetermined number of word hypotheses with the detected word hypothesis and the topic identification information in the predetermined discourse space, and among the detected word hypotheses, the topic in the predetermined discourse space It is determined whether there is a match with the specific information (steps S603 and S604). If there is a match, the speech recognition unit 200 outputs the matching word hypothesis as a recognition result (step S605). On the other hand, when there is no match, the speech recognition unit 200 outputs the word hypothesis having the maximum likelihood as the recognition result according to the acquired likelihood of the word hypothesis (step S606).

[Voice recognition dictionary storage]
Returning to FIG. 3 again, the description of the configuration example of the conversation control device 1 will be continued.
The 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. 1 and 7. FIG. 7 is a partial enlarged block diagram of the conversation control device 1, and is a block diagram illustrating a specific configuration example of the conversation control unit 300 and the sentence analysis unit 400. In FIG. 7, only the conversation control unit 300, the sentence analysis unit 400, and the conversation database 500 are shown, and the display of other components is omitted.

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

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

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

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

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

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

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

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

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

    [Topic specific information]

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

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

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

  Each answer sentence 830 is associated with an emotion condition parameter 840. 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 “anger” emotion is indicated. The character data “B” is assigned as a flag, the character data “C” is assigned as an emotion flag indicating an emotion of “anger”, the character data “D” is assigned as an emotion flag indicating an emotion of “joy”, and so on. . The conversation control device 1 according to the present embodiment refers to the emotion flag 850 and controls the emotion of the character (pseudo personality) provided by the conversation control device 1.

  In this embodiment, the emotion flag 850 is associated with the answer sentence 830. However, the present invention is established even when the emotion flag 850 is associated with the topic title 820 and the topic identification information 810.

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

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

Further, topic specific information 810C ₁ (= “director”), topic specific information 810C ₂ (= “starring”), topic specific information 810C ₃ (= “distribution company” for 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 the conversation control device 1 according to the present embodiment identifies certain topic identification information by referring to the stored content of the conversation database 500, the other topic identification information stored in association with the topic identification information and It is possible to search and extract topic titles and answer sentences of topic identification information at high speed.

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

The topic identification information 810D ₁ , 810D ₂ , 810D ₃ ,... Has a plurality of different topic titles 820 ₁ , 820 ₂ ,..., Topic titles 820 ₃ , 820 ₄ ,..., Topic titles 820 ₅ , 820 ₆ ,. is doing.

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

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

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 the first specific information 1301, the second specific information 1302, and the 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, but is further limited to other specific information (fourth specific information and it). You may make it have the above.

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

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

The conversation control unit 300 controls the data transfer between the constituent elements (the speech recognition unit 200, the sentence analysis unit 400, the conversation database 500, and the speech recognition dictionary storage unit 600) in the conversation control device 1, and responds to the utterance. Has a function to determine and output an answer sentence.
In the present embodiment, as shown in FIG. 7, the conversation control unit 300 includes a management unit 310, a topic specifying information search unit 320, an abbreviated sentence complement unit 330, a topic search unit 340, and an answer acquisition unit 350. And have.

  The management unit 310 controls the entirety of the conversation control unit 300 and receives game history information from the game history information storage unit 8, and based on this, performs conversation control (determination of topic identification information, identification of topic title, answer sentence) For example).

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 extracted from the morpheme included in any one of the first specific information, the second specific information, or the third specific information constituting the topic title based on the topic title of interest. It may be included in the information.

[Topic Search Department]
When the topic title 820 is not determined by the abbreviated sentence complementing unit 330, the topic search unit 340 collates the first morpheme information with each topic title 820 corresponding to the user input sentence topic specifying information, and each topic title 820 The topic title 820 that is most suitable for the first morpheme information is searched for.

  Specifically, the topic search unit 340 to which the search command signal is input from the abbreviated sentence complement unit 330 is used based on the user input sentence topic identification information and the first morpheme information included in the input search command signal. The topic title 820 most suitable for the first morpheme information is searched from the topic titles 820 associated with the person input sentence topic identification information. The topic search unit 340 outputs the searched topic title 820 to the answer acquisition unit 350 as a search result signal.

  FIG. 15 is a diagram illustrating a specific example of the topic title 820, the answer sentence 830, the emotion condition parameter 840, and the emotion flag 850 associated with certain topic identification information 810 (= “Sato”). As shown in FIG. 15, for example, the topic search unit 340 includes the topic identification information 810 (= “Sato”) in the input first morpheme information “Sato, I like”, so the topic identification information 810 (= First, the first morpheme information that is input as each topic title (820) 1-1, 1-2,... Associated with the topic specifying information 810 (= “Sato”) Match “Sato, I like”.

  The topic search unit 340, based on the comparison result, the topic title (820) that matches the input first morpheme information “Sato, I like” from among the topic titles (820) 1-1 and 1-2. Specify 1-1 (Sato; *; likes). The topic search unit 340 outputs the searched topic title (820) 1-1 (Sato; *; likes) to the answer acquisition unit 350 as a search result signal.

[Answer section]
The answer acquisition unit 350 acquires an answer sentence 830, an emotion condition parameter 840, and an emotion flag 850 associated with the topic title 820 based on the topic title 820 searched by the topic search unit 340. 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. 15, for example, when the topic title 820 searched by the topic search unit 340 is the topic title (820) 1-1 (Sato; “Spoken sentence type” (for example, DA) determined by the input type determination unit 440 from among the answer sentences (830) 1-1 (DA, TA, etc.) associated with the title (820) 1-1. Identify the answer type (DA) that matches 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 created in this question format (Q) include a question sentence that is replied to the utterance content, or a question sentence that asks 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).

  Further, the answer acquisition unit 350 refers to emotions from a plurality of answer sentence candidates corresponding to the type of utterance sentence (for example, DA) specified with reference to the emotion state information stored at that time by the emotion state information management means 7. The emotion condition parameter 840 describing the condition that matches the state information is specified (for example, “all 2 or less”), and the content corresponding to the emotion condition parameter 840 (for example, “I also like Sato”) get.

  Furthermore, the answer acquisition unit 350 outputs the content of the acquired answer sentence 830 to the management unit 310 as an answer sentence signal. The management unit 310 to which the answer sentence signal is input from the answer acquisition unit 350 outputs the input answer sentence signal to the voice output unit 6. In addition, the answer acquisition unit 350 outputs the acquired emotion flag 850 to the emotion state information management means 7. Thereby, it is possible to describe the change in the emotion of the character for each output of the answer sentence 830.

[Emotional state information management means]
Next, a configuration example of the emotional state information management unit 7 will be described with reference to FIG. FIG. 16 is a functional block diagram illustrating a configuration example of the emotion state information management unit 7.

  The emotion state information management means 7 receives an emotion flag 850 that describes a change in character's emotion from the conversation control device 1, receives game state information from the game device 2, and rewrites the emotion state information. 7A, emotion state information storage unit 7B for storing emotion state information, and emotion state information for notifying all or part of the emotion state information to conversation control device 1, game device 2, and game history information storage means 8 And a notification unit 7C.

  When emotion state information update unit 7A receives emotion flag 850, emotion state information update unit 7A rewrites emotion state information so that the count value of the emotion flag is changed by a predetermined value (for example, +1). When the emotion state information update unit 7A receives the game state information (for example, information indicating the user's win), the emotion state information update unit 7A rewrites the emotion state information with a predetermined content (for example, sets the count value of the emotion flag of “anger” to 10). Or increase the count value of emotion flags of “sadness” and “anger” twice the current value).

  FIG. 17 shows a data configuration example of emotion state information stored in the emotion state information storage unit 7B. The emotion state information 1700 has a cumulative value 1702 for each emotion flag type 1701. The cumulative value 1702 can be increased or decreased for each emotion flag. For example, each time the emotion state information management means 7 receives an emotion flag indicating “joy”, the value of the accumulated value 1702 is incremented. Also, the cumulative value 1702 is increased or decreased according to the game state information from the game apparatus 2. How to increase / decrease is stored in advance by the emotional state information updating unit 7A as a program, and a predetermined increase / decrease process is executed according to this program.

  The emotional state information notification unit 7C notifies the conversation control device 1, the game device 2, and the game history information storage unit 8 of all or part of the emotional state information 1700 stored in the emotional state information storage unit 7B. The conversation control device 1 refers to the received emotion state information 1700, acquires the content of the answer sentence 830 that satisfies the emotion condition parameter 840 stored in the conversation database 500, and outputs it as a reply to the user. Therefore, this game system GS can output an answer having contents according to the emotion of the character, and can establish a natural conversation.

In addition, when the game apparatus 2 receives the emotion state information 1700, the game apparatus 2 performs a character display process so as to indicate the display and operation of the character according to the emotion state information 1700. Thereby, this game system GS can display the character according to the change of the emotion of the character. The game history information storage means 8 stores the notified emotion state information as a part of the game history information, and the emotion state information stored as a part of the game history information is used when the next game is resumed. Since the answer sentence is selected with reference to the above, it is possible to perform the conversation control reflecting the conventional emotional state.

[Game history information storage means]
As described above, the game history information storage unit 8 has a function of storing game history information. FIG. 18 shows an example of the data structure of the game history information stored in the game history information storage unit 8. In the example shown in this figure, the game history information 1800 has a character ID 1801, emotion state information 1802, game battle situation information 1803, last play date 1804, play frequency 1805, total number of plays 1806, and total win / loss 1807.

  The character ID 1801 is an identifier for specifying a character, and is required in a game in which any of a plurality of characters is a user's opponent. In such a case, game history information 1800 may be created and stored for each character. Emotion state information 1802 is emotion state information 1700 notified from emotion state information management means 7. The game game status information 1803, the last play date and time 1805, the play frequency 1805, the total play count 1806, and the total win / loss 1807 are information passed from the game apparatus 2 to the game history information storage unit 8.

  The game history information 1800 is referred to by the conversation control device 1 at a predetermined timing (for example, at the start of the game or when the game is resumed), and is used to return an answer sentence corresponding to the game history information 1800 to the user. The

[Game system operation example]
An example of the operation of the game system GS according to the present embodiment having the above configuration will be described with reference to FIG. FIG. 19 is a flowchart showing main processes related to conversation control and management of emotional state information in the operation example of the game system GS according to the present embodiment. In addition, although the process regarding a game progress is not shown about this flowchart, the game device 2 is performing the game progress separately according to a user's input.

  When the user inputs an instruction to the game system GS to play a game, first, a game start process, which is various processes necessary for starting the game, is performed (step S1901). Various processes necessary for starting the game include, for example, loading a conversation control program and loading a game program.

  Next, the game system GS reads the game history information from the game history information storage unit 8 and refers to the game history information (step S1902). If the game history information storage means 8 is a separate storage medium that is separable or detachable from the game system GS, the game system GS will “read the card. Set the card. May be displayed to allow the user to perform a process or operation necessary for reading the game history information.

  Next, the game system GS performs a conversation control process based on the game history information so that the conversation control device 1 outputs an answer according to the game history information (step S1903). In the conversation control process based on the game history information in step S1903, an answer sentence is output according to the game history information, instead of outputting an answer sentence according to the user's utterance. The following are some examples of outputting an answer sentence according to the game history information.

  (1) When the game battle situation information 1803 indicates that the previous character has lost, the game system GS outputs the content “This time, I will win!” While the previous character won. If so, the content “Please do your best now?” Is output as an answer sentence. By such a change in the answer sentence, it is possible to produce an effect as if the character memorizes the previous game result.

  Various methods can be considered for the conversation control device 1 to select such an answer sentence according to the game history information 1800. As an example, the topic specifying information 810 = “game situation information”, the topic specifying information 810 Corresponding topic title 820 = “(character; lose; *)” and answer sentence “Now, win!” And topic title 820 = “(character; win; *)” and answer sentence “Now. The content of “Do your best?” Is registered in the conversation database 500, and the topic control information and the data “character loses” or “character wins” corresponding to the utterance information are transferred to the conversation control unit 300. It is done.

(2) An example in which an answer sentence is output according to the play frequency 1805 included in the game history information 1800 will be given.
When the play frequency 1805 is “0”, that is, when it is described in the game history information 1800 that the game has not been played once in a predetermined period, the game system GS reads “Long time no see ...” On the other hand, if the play frequency 1805 is greater than or equal to the predetermined value N, that is, if the game history information 1800 describes that the game is played N or more times within a predetermined period, The system GS outputs an answer sentence with the content “Okay, I am very enthusiastic.” Such a change in the answer sentence can produce an effect as if the character recognizes the intimacy with the user.

  As an example of a method for the conversation control device 1 to select such an answer sentence according to the game history information 1800, topic specification information 810 = “play frequency”, topic title associated with the topic specification information 810 820 = “(Frequency of play; *; 0 times)” and the response sentence “Long time no see ...”, topic title 820 = “(Frequency of play; *; N times or more)” and the response sentence “Yoshiyoshi ”Is registered in the conversation database 500, and the topic control information and the data corresponding to the utterance information“ play frequency is 0 ”or“ play frequency is A method of passing “N times or more” is conceivable.

(3) An example in which an answer sentence is output according to the total play count 1806 included in the game history information 1800 will be given.
When the total play count 1806 is “0”, that is, when it is described in the game history information 1800 that it is the first game, the game system GS gives an answer sentence with the content “Nice to meet you.” On the other hand, when the total play count 1806 is “1”, that is, when the game history information 1800 describes that the game is the second or later game, the game system GS "Answer!" Is output. Such a change in the answer sentence can produce an effect as if the character recognizes the reunion with the user.

  In addition, as an example of a method in which the conversation control device 1 selects such an answer sentence according to the game history information 1800, topic specification information 810 = “total number of times played”, a topic associated with the topic specification information 810 Title 820 = “(total number of times; *; 0 times)” and reply sentence “Nice to meet you.” And topic title 820 = “(total number of times; *; more than two times)” and reply sentence “Acha ~, The content of “I got it again!” Is registered in the conversation database 500, and the conversation control unit 300 stores the topic specifying information and data corresponding to the utterance information “the total number of times is zero” or “the total number of times is two times or more. "Can be considered.

When the conversation control process based on the game history information (step S1903) ends, a conversation control process for responding to a normal user utterance is performed thereafter.
When there is an utterance from the user (YES in step S1904), the conversation control device 1 accepts the 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 S1905).

  Next, the conversation control device 1 performs a conversation control process for returning an answer sentence in response to the received user utterance (step S1906).

[Conversation control method]
The conversation control process (step S1906) by the conversation control apparatus 1 having the above-described configuration can be performed by a procedure as 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 identification 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 answer acquisition unit 350 outputs the acquired answer sentence 830 to the output unit 600 via the management unit 310. The output unit 600 to which the answer sentence is input from the management unit 310 outputs the input answer sentence 830 (step S2010).
Thus, the conversation control process (step S1906) ends.

Returning to FIG. 19, the description of the operation example of the game system GS is continued.
When the conversation control process (step S1906) ends, the conversation control device 1 passes the emotion flag 850 associated with the answer sentence 830 to the emotion state information management unit 7, and the emotion state information management means 7 sends the emotion flag 850 to the emotion state information management unit 7. A predetermined process is performed to reflect the emotion state information 1700 to rewrite the emotion state information 1700 (step S1907).

Next, the game system GS determines whether or not the game end condition is satisfied (step S1908). If the game end condition is satisfied (step S1908, YES; for example, the end of the game time), the game system GS writes the game history information 1800 to the game history information storage means 8 (step S1909), and thereafter End the game. Note that the game history information 1800 may not be stored directly in the game history information storage means 8, but once the game history information 1800 is stored in a storage device (not shown) in the game system GS, The game history information 1800 may be copied or transferred from the storage device to the game history information storage means 8.
On the other hand, when the game end condition is not satisfied (step S1908, NO), the process returns to step S1904 again to wait for the user utterance.

[Modification]
The game system GS is also established as a so-called server / client format. FIG. 21 is a block diagram illustrating an example in which the game system GS is configured by a server and a client.

  The game system GS includes a game server 2101 and a user terminal device 2102 that can be connected to the game server 2101 via the communication network 2100.

  The game server 2101 has a conversation control device 1, a game device 2, and emotional state information management means 7. A user terminal device 2102 has voice input means 3, non-voice input means 4, and image output. Means 5, sound output means 6, and game history information storage means 8 are provided. The user terminal device 2102 may be any information processing device having a network connection function. For example, a computer, an arcade game machine, a home game machine, a portable game machine, a mobile phone, a PDA (Personal Data Assistant), and the like.

  Voice (such as speech) and non-voice (such as game commands) input from the user terminal device 2102 are sent to the game server 2101 via the communication network 2102. The game server 2101 transmits data such as an answer sentence, a character image, and a game screen image to the user terminal device 2102 via the communication network 2100 according to the received voice (speech etc.) and non-voice (game command etc.).

  Upon receiving data such as an answer sentence, a character image, and a game screen image, the user terminal device 2102 outputs an answer sentence and displays the character and the game screen.

  The game history information stored in the game history information storage unit 8 of the user terminal device 2102 is sent to the game server 2101 at a predetermined timing such as at the start of the game, and the game server 2101 sends an answer sentence according to the game history information as described above. Is selected and returned to the user terminal device 2102. In addition, the game server 2101 transmits the latest game history information to the user terminal device 2102 at a predetermined timing such as when the game ends, and the user terminal device 2102 stores the received game history information in the game history information storage unit 8. Save it for the next game.

  According to such a configuration, it is possible to provide a game system GS that can return an answer according to the previous game play to the user regardless of when and where the user terminal device 2102 is connected to the communication network 2100 to play a game. In the server-client game system GS, the user terminal device 2102 itself serves as a storage medium that can be separated from or attached to the game system GS.

As another modification, in the example shown in FIG. 21, the game history information storage unit 8 can be a storage medium that is separable from or removable from the user terminal device 2102.
[Others]
(1) The configurations and operations of the speech recognition unit 200, the conversation control unit 300, and the sentence analysis unit 400 of the conversation control device 1 are not limited to those described in the above embodiment, and the user can use the conversation database means. Any speech recognition unit 200, conversation control unit 300, and sentence analysis unit 400 other than those described in the embodiment as long as it returns an answer corresponding to the utterance content, the conversation control according to the present invention. It can be used as a component of the device 1.
(2) In the description of the above embodiment, the input of the utterance to the conversation control device 1 has been described as being by voice, but the input of the utterance to the conversation control device 1 is not limited to voice, but a keyboard, a touch panel An utterance is input to the conversation control device 1 as character string data by the non-voice input means 4 such as a pointing device, and the conversation control device 1 answers the utterance input as the character string data using the conversation database 500. The present invention is also established as a configuration for outputting a sentence.

Block diagram showing a configuration example of a game system Functional block diagram showing a configuration example of a game device Functional 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 explaining the type of answer text The figure which shows the specific example of the topic title matched with a certain topic specific information, an answer sentence, an emotion condition parameter, and an emotion flag Functional block diagram showing a configuration example of emotion state information management means The figure which shows the data structural example of the emotion state information memorize | stored in an emotion state information storage part The figure which shows the data structural example of game history information Flow chart showing the main operations of the game system Flow chart showing an example of conversation control processing Block diagram showing another configuration example of the game system

Explanation of symbols

GS ... Game system 1 ... Conversation control device 2 ... Game device 3 ... Audio input means 4 ... Non-voice input means 5 ... Image output means 6 ... Audio output means 7 ... Emotion state information management means 8 ... Game history information storage means 200 ... Speech recognition unit 300 ... Conversation control unit 400 ... Sentence analysis unit 500 ... Conversation database 600 ... Speech recognition dictionary storage unit

Claims (11)

Input means for receiving utterances from the user, conversation processing means for outputting an answer sentence in response to the utterances from the user, game processing means for controlling the character and executing the game, and emotion state information indicating the emotion of the character In a game machine having emotion state information management means for processing and game history information storage means for storing game history information,
The conversation processing means includes
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 the topic and subject of conversation between the user and the conversation processing means;
A topic title that is composed of a morpheme composed of a single character, a plurality of character strings, or a combination thereof, and that associates an answer sentence to the user with an emotion flag that constitutes the emotion state information is associated with the discourse history. A conversation database means for storing a plurality of topic specific information determined by
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;
The morpheme information extracted by the morpheme extraction unit, the topic identification information stored in the conversation database means, and the game history information stored in the game history information storage means are collated, and morpheme information is selected from each topic identification information. Means for selecting the answer sentence to the user by selecting topic identification information that matches the morpheme that constitutes
Have
The emotional state information management means includes
Receives an emotion flag associated with the selected answer sentence, receives game state information about a game state from the game processing means, and updates the emotion state information based on the received emotion flag and the game state information An emotional state information update unit,
An emotional state information storage unit for storing emotional state information updated by the emotional state information update unit;
An emotional state information notifying unit for notifying at least a part of the emotional state information stored in the emotional state information storing unit to the conversation processing means, the game processing means, and the game history information storing means;
Have
The game history information storage unit stores cumulative and historical game history information of a previous game including a game result, a game play last date and time, and an emotion notified from the emotion state information notification unit. Store state information as part of game history information,
The game processing means controls a character based on emotion state information notified from the emotion state information notification unit.
A game machine characterized by that . The game machine according to claim 1, wherein the game history information storage unit is a storage medium removable from the game machine. The game machine according to claim 1, wherein the conversation processing unit selects an answer sentence with reference to the game history information stored in the game history information storage unit before the game is started . 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 any one of claims 1 to 3, 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 4 . Input means for receiving utterances from the user, conversation processing means for outputting an answer sentence in response to the utterances from the user, game processing means for controlling the character and executing the game, and emotion state information indicating the emotion of the character Emotion history information management means for processing, and game history information storage means for storing cumulative and historical game history information of previous games including game results, game play last date and time, and the number of game plays, Conversation processing means is composed of means for storing a discourse history that is information specifying the topic and subject of conversation between the user and the conversation processing means, and a morpheme consisting of one character, a plurality of character strings, or a combination thereof And a topic title formed by associating an answer sentence to the user with an emotion flag constituting the emotion state information is associated with the topic title. A method for executing a game in conjunction with a game machine comprising and a conversation database means for storing a plurality of topic specifying information determined by the conversation history,
The conversation processing means refers to the game history information stored in the game history information storage means;
The conversation processing means outputting an answer sentence based on the referenced game history information;
The recognition unit of the conversation processing means 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 means 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 compares the morpheme information extracted by the morpheme extraction unit with the topic identification information stored in the conversation database unit, and constitutes morpheme information from each topic identification information Searching for topic specific information matching the morpheme;
The reply acquisition unit of the conversation processing means acquires an answer sentence associated with the topic identification information searched by the topic identification information search unit;
The emotion state information management means receives an emotion flag associated with the answer sentence acquired by the answer acquisition unit, receives game state information related to a game state from the game processing means, and receives the received emotion flag and Updating emotion state information based on the game state information;
The game history information storage means receiving and storing updated emotion state information from the emotion state information management means at the end of the game;
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 means 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 6 further comprising: After the step in which the topic specifying information search unit of the conversation processing means searches for the topic specifying information, the abbreviated sentence complementing unit of the conversation processing means obtains the topic specifying information included in the previous topic specifying information and the previous answer sentence. The method according to claim 6, further comprising generating a plurality of types of complemented morpheme information by using the complement of the morpheme information. The method according to claim 6, wherein the game history information storage means is a storage medium removable from a game machine . The program for functioning a computer as a game machine of any one of Claims 1 thru | or 5 . The program for making a computer perform the method of any one of Claim 6 thru | or 9 .

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