Patent Publication Number: US-8996373-B2

Title: State detection device and state detecting method

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2010-029119, filed on Dec. 27, 2010, the entire contents of which are incorporated herein by reference. 
     FIELD 
     A certain aspect of the embodiments discussed herein is related to a state detection device and a state detecting method. 
     BACKGROUND 
     There has been a technique for recognizing the emotion etc. of a speaker from the voice of the speaker. 
     Relating to the technique above, there is an utterance modified speech recognition device having a high recognition performance even when there is a small amount of speech data used in learning an utterance modification model. The utterance modified speech recognition device learns an utterance modification model representing a modification of a phoneme spectrum occurring in the voice having an utterance modification. Then, the utterance modified speech recognition device outputs a standard modified voice model by performing a spectrum modifying process using an utterance modification model on a standard voice model without utterance modifications. Next, the utterance modified speech recognition device performs a recognizing process on an utterance modified voice feature vector time series obtained by performing a sound analysis on an input voice signal using a standard modified voice model and a standard voice model without utterance modifications. 
     Furthermore, there is a speech recognition system known for recognizing the level of the emotion of a speaker. The speech recognition system includes, for example, a voice analysis unit, a dictionary unit, a acoustic model unit, an utterance modifying emotion model unit, and a voice-emotion recognition unit. Then, the dictionary unit stores a word for speech recognition. The acoustic model unit stores a model for use in the speech recognition. Practically, it stores a acoustic model indicating the correspondence between a character and a phoneme used in the dictionary unit. The utterance modifying emotion model unit stores an utterance modifying emotion model indicating the correspondence between a character and a phoneme used in the dictionary unit when the emotion has changed. The voice-emotion recognition unit stores the level indicating a word in phoneme units and the strength of the emotion. 
     Then, the speech recognition system compares for the voice analysis result of the input voice analyzed by the voice analysis unit between the acoustic model and the dictionary by phoneme units connected by a model connecting unit, and picks up the most likely word in the dictionary enrolled in the dictionary unit. Furthermore, the speech recognition system selects from the voice-emotion recognition unit the level indicating the strength of the emotion represented by the input voice of the picked up word. 
     In addition, in the speech recognition devices which recognizes voice by comparing a synthetic voice model to which noise adaptation and speaker adaptation are applied with a feature vector sequence obtained by the uttered voice during the utterance, a speech recognition device capable of reducing the computational complexity when noise adaptation, speaker adaptation, etc. are performed on an initial voice model is well known. 
     [Patent Document 1] Japanese Laid-open Patent Publication No. 08-211887 
     [Patent Document 2] Japanese Laid-open Patent Publication No. 11-119791 
     [Patent Document 3] Japanese Laid-open Patent Publication No. 2004-109464 
     [Non-patent Document 1] “Speech recognition System” by Kiyohiro Kano, Katsunobu Ito, Tatsuya Kawahara, Kazuya Takeda, and Mikio Yamamoto, and published by Ohmsha 
     [Non-patent Document 2] “Introduction to Cluster Analysis” by Sadaaki Miyamoto, and published by Morikita Publication 
     [Non-patent Document 3] Douglas A. Reynolds/Richard C. Rose, “Robust text-independent speaker identification using Guassian mixture speaker models” IEEE Trans. on Speech and Audio Process, vol. 3, no. 1, pp. 72-83 1995 
     [Non-patent Document 4] Douglas A. Reynolds/Thomas F. Quatieri/Robert B. Dunn, “Speaker verification using adapted Gaussian Mixture models”, Digital Signal Processing, vol. 10, pp. 19-41 2000 
     SUMMARY 
     According to an aspect of the embodiment, the state detection device includes the following components. 
     A basic model storage unit stores a basic model obtained by modeling the feature of the voice acquired from a plurality of unspecific speakers. 
     A correspondence information storage unit stores the correspondence information indicating the correspondence between a first unspecific speaker model and a second unspecific speaker model. The first unspecific speaker model is obtained by modeling the speech features of unspecific speakers in an undepressed state. The second unspecific speaker model is obtained by modeling speech features of unspecific speakers in a depressed state. 
     A first model generation unit extracts the speech features of a specific speaker in the undepressed state, and adjusts the basic model so that the extracted feature is indicated, thereby generates a first specific speaker model obtained by modeling the speech features of the specific speaker in the undepressed state. 
     A second model generation unit reflects the amount of displacement from the first unspecific speaker model to the second unspecific speaker model on the first specific speaker model using the correspondence information. Thus, the second model generation unit models the speech features of the specific speaker in the depressed state, and generates a second specific speaker model. 
     A likelihood calculation unit calculates a first likelihood as the likelihood of the first specific speaker model with respect to the feature of input voice, and a second likelihood as the likelihood of the second specific speaker model with respect to the input voice. 
     A state determination unit determines the state of the speaker of the input voice using the first likelihood and the second likelihood. 
     The object and advantages of the embodiment will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the embodiment, as claimed. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is an explanatory view of the outline of the state detection device considered by the applicant; 
         FIG. 2  is an explanatory view of the outline of the state detection device considered by the applicant; 
         FIG. 3  is an explanatory view of a state detection device  300 ; 
         FIG. 4  is an explanatory view of a state detection device  400 ; 
         FIG. 5  is a practical example of a correspondence table  431 ; 
         FIG. 6  is an explanatory view of generating a static state model for a specific speaker and an abnormal state model for a specific speaker; 
         FIG. 7  is a flowchart of the process performed when a speaker is enrolled; 
         FIG. 8  is a flowchart of the process of detecting the state of a speaker; 
         FIG. 9  is an explanatory view of the outline of an advance preparation; 
         FIG. 10  is an explanatory view of the outline of the process performed in an advance preparation as illustrated in  FIG. 9 ; 
         FIG. 11  is a flowchart of the process of an advance preparation; 
         FIG. 12  is an explanatory view of a state detection device  1200 ; 
         FIG. 13  is a flowchart of an operation of the state detection device  1200 ; 
         FIG. 14  is a flowchart of an operation of the state detection device  1200 ; 
         FIG. 15  is an example of a configuration of the state detection device described with reference to an embodiment; and 
         FIG. 16  is an example of a configuration of a mobile telephone  1600  including the state detection device described with respect to an embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     For example, the utterance modified speech recognition device above performs a recognizing process using a standard modified voice model and a standard voice model without utterance modifications, but a standard voice model without utterance modifications is not a model specified for a specific speaker. Therefore, it is hard to consider that a standard modified voice model generated from a standard voice model without utterance modifications is a model sufficiently specified for a specific speaker. Accordingly, the device has a recognition rate lower than the case in which a standard modified voice model and a standard voice model without utterance modifications specified for a specific speaker are used. 
     In the above-mentioned speech recognition system, an utterance modified emotion model, a word in a phoneme unit, and a emotion level are not specialized for a specific speaker. Therefore, the recognition performance of the feeling level about a specific speaker has been low. 
       FIGS. 1 and 2  are explanatory view of the outline of the state detection device considered by the applicant. 
     A state detection device  100  illustrated in  FIG. 1  includes a storage unit  110  storing a basic model, a storage unit  120  storing a static state model for a specific speaker, a storage unit  130  storing an abnormal state model for a specific speaker, a voice analysis unit  140 , a likelihood calculation unit  150 , and a likelihood comparison unit  160 . 
     In  FIG. 1 , a device to be used is described for each of the processes for an “advance preparation”, an “enrolment of a speaker”, and “detection of the state of a speaker” for comprehensibility of the state detection device  100 . However, it is not to limit the configuration of the state detection device  100 , for example, the arrangement of the devices, the connection among the devices, etc.  FIG. 2  illustrates the same gist. 
     The basic model stored in the storage unit  110  refers to the information about the speech features of a standard speaker. The basic model is expressed by a model obtained by quantizing a feature parameter extracted from among a large amount of speech data acquired from unspecific speakers using a Gaussian mixture model (hereafter referred to as a “GMM”). The basic model is generated in advance and stored in the storage unit  110 . The feature parameter is described in detail with reference to  FIG. 7 . 
     The static state model for a specific speaker stored in the storage unit  120  refers to the information obtained by quantizing the feature parameter extracted from the speech data acquired from a specific speaker in the static state using the GMM. 
     The abnormal state model for a specific speaker stored in the storage unit  130  refers to the information obtained by quantizing the feature parameter extracted from the speech data acquired from a specific speaker in the abnormal state using the GMM. 
     The state detection device  100  needs an enrolment of a speaker whose detection is to be detected in performing a state detecting process. By the enrolment of a speaker, the state detection device  100  generates a static state model for a specific speaker and an abnormal state model for a specific speaker. 
     For example, a user of the state detection device  100  enrolls utterance data  101  of a speaker F in the static state and utterance data  102  of the speaker F in the abnormal state in the state detection device  100 . Then, the state detection device  100  adapts the basic model to the utterance data  101  of the speaker F in the static state, and generates a static state model for a specific speaker about the speaker F. Then, the state detection device  100  stores the generated static state model for a specific speaker in the storage unit  120 . 
     “To adapt” is to adjust a parameter included in the basic model by the parameter obtained from the feature parameter extracted from the utterance data  101  of the speaker F in the static state. 
     Furthermore, the state detection device  100  adapts the static state model for a specific speaker about the speaker F to utterance data  102  of the speaker F in the abnormal state, and generates an abnormal state model for a specific speaker about the speaker F. Then, the state detection device  100  stores the generated abnormal state model for a specific speaker in the storage unit  130 . 
     When the enrolment of the speaker is completed, the state detection device  100  detects the state of the speaker F as described below. 
     For example, when the utterance data of the speaker F is input, the voice analysis unit  140  extracts the feature parameter from the utterance data of the speaker F. Then, the likelihood calculation unit  150  calculates the likelihood of the static state model for a specific speaker about the speaker F with respect to the extracted feature parameter. Furthermore, the likelihood calculation unit  150  calculates the likelihood of the abnormal state model for a specific speaker about the speaker F with respect to the extracted feature parameter. 
     The likelihood comparison unit  160  compares the two likelihoods calculated by the likelihood calculation unit  150  with each other, and determines the state of the speaker F, that is, whether the speaker F is in the static state or in the abnormal state. The likelihood comparison unit  160  outputs the determination result to a specified device etc. 
     As described above, since the state detection device  100  generates a static state model for a specific speaker and an abnormal state model for a specific speaker during the enrolment of the speaker, it needs no advance preparation for using the state detecting function. The advance preparation is a preparation needed, for example, before shipping as a product the state detection device  100  or a device including the state detection device  100 . 
     Since the state detection device  100  detects the state of a speaker using a static state model for a specific speaker and an abnormal state model for a specific speaker, the state of a speaker may be detected with high accuracy. 
     However, it is a heavy load for a user to enrol the voice of a specific speaker in the abnormal state. 
     A state detection device  200  illustrated in  FIG. 2  includes a storage unit  210  storing a static state model for unspecific speakers, a storage unit  220  storing an abnormal state model for unspecific speakers, a voice analysis unit  140 , a likelihood calculation unit  230 , and a likelihood comparison unit  160 . 
     The static state model for unspecific speakers stored in the storage unit  210  refers to the information obtained by quantizing the feature parameter extracted from the speech data acquired from a number of unspecific speakers in the static state using the GMM. 
     The abnormal state model for unspecific speakers stored in the storage unit  220  refers to the information obtained by quantizing the feature parameter extracted from the speech data ad from a number of unspecific speakers in the abnormal state using the GMM. 
     The state detection device  200  needs an advance preparation for detecting a state. In the advance preparation, a static state model for unspecific speakers and an abnormal state model for unspecific speakers for use in detecting a state are generated. 
     The advance preparation may be performed by the information processing device  250  capable of communicating data directly or indirectly with the state detection device  200 . The state detection device  200  itself may perform the advance preparation. 
     The information processing device  250  includes a storage unit  251  storing a basic model, a storage unit  252  storing a static state model for unspecific speakers, and a storage unit  253  storing an abnormal state model for unspecific speakers. 
     The information processing device  250  generates a static state model for unspecific speakers by adapting the basic model to a number of pieces of static state speaker data  254 . The information processing device  250  stores the generated static state model for unspecific speakers in the storage unit  252 . The information processing device  250  generates an abnormal state model for unspecific speakers by adapting a static state model for unspecific speakers to a number of pieces of abnormal state speaker data  255 . The information processing device  250  stores the generated abnormal state model for unspecific speakers in the storage unit  253 . 
     The static state speaker data  254  is speech data acquired from unspecific speakers in the static state. The abnormal state speaker data  255  is speech data acquired from unspecific speakers in the abnormal state. 
     When a static state model for unspecific speakers and an abnormal state model for unspecific speakers are generated, the information processing device  250  stores the static state model for unspecific speakers stored in the storage unit  252  in the storage unit  210  provided for the state detection device  200 . The information processing device  250  stores the abnormal state model for unspecific speakers stored in the storage unit  253  in the storage unit  220  provided in the state detection device  200 . 
     When the above-mentioned advance preparation is completed, the state detection device  200  detects the state of the speaker as described below when the device may detect the state. 
     For example, when the utterance data of the speaker F is input, the voice analysis unit  140  extracts the feature parameter from the utterance data of the speaker F. Then, the likelihood calculation unit  230  calculates the likelihood of the static state model for unspecific speakers with respect to the extracted feature parameter. Furthermore, the likelihood calculation unit  230  calculates the likelihood of the abnormal state model for unspecific speakers with respect to the extracted feature parameter. 
     In the likelihood comparison unit  160 , the likelihood comparison unit  160  compares the two likelihoods calculated by the likelihood calculation unit  230  with each other, and determines the state of the speaker F, that is, whether or not the speaker F is in the static state or in the abnormal state. The likelihood comparison unit  160  outputs a determination result to a specified device etc. 
     As described above, the state detection device  200  generates in advance a static state model for unspecific speakers and an abnormal state model for unspecific speakers. Then, the state detection device  200  detects the state of the speaker using the static state model for unspecific speakers and the abnormal state model for unspecific speakers. Therefore, the state detection device  200  does not need the enrolment of the speaker needed by the state detection device  100 . As a result, no heavy load is imposed on the state detection device  200  by the enrolment of a speaker. 
     However, since the state detection device  200  uses the static state model for unspecific speakers and the abnormal state model for unspecific speakers in detecting the state of a speaker, it indicates much lower accuracy in detecting the state of a specific speaker than the state detection device  100 . 
     From the description above, the applicant extracts the problem to provide a state detection device capable of detecting the state of a specific speaker with high accuracy using the information included in the voice with the lower load imposed on the necessary process for detecting the state of a speaker such as an enrolment of a speaker etc. 
     An example of the present embodiment is described below with reference to  FIGS. 3 through 16 . The embodiment described below is simply an example, and does not intend to exclude the application of variations and modifications not clearly described below. That is, the present embodiment may be realized in many variations within the scope of the gist of the present embodiment. 
     Embodiments 
       FIG. 3  is an explanatory view of the state detection device  300  according to an embodiment of the present invention. 
     The state detection device  300  includes a basic model storage unit  301 , a correspondence information storage unit  302 , a first model generation unit  303 , a second model generation unit  304 , a likelihood calculation unit  305 , and a state determination unit  306 . 
     The basic model storage unit  301  is a storage device for storing a basic model obtained by modeling the feature of voice acquired from a plurality of unspecific speakers. The basic model is, for example, information which may be defined using the GMM etc. 
     The correspondence information storage unit  302  is a storage device for storing the correspondence information about the correspondence between the first unspecific speaker model and the second unspecific speaker model. The first unspecific speaker model is information obtained by modeling the feature of the voice of unspecific speakers in the undepressed state. In addition, the second unspecific speaker model is the information obtained by modeling the feature of the voice of unspecific speakers in the depressed state. The first unspecific speaker model and the second unspecific speaker model may be, for example, defined using the GMM etc. Therefore, the correspondence between the first unspecific speaker model and the second unspecific speaker model may be expressed by, for example, a parameter included in the GMM. The first model generation unit  303  extracts the feature of the voice of a specific speaker in the undepressed state, and adjusts the basic model so that it expresses the extracted feature, thereby generating the first specific speaker model obtained by modeling the feature of the voice of a specific speaker in the undepressed state. For example, the first model generation unit  303  adjusts the parameter included in the basic model so that it indicates the feature of the voice of a specific speaker in the undepressed state. 
     The second model generation unit  304  reflects the amount of displacement from the first unspecific speaker model to the second unspecific speaker model according to the correspondence information on the first specific speaker model. Thus, the second model generation unit  304  generates the second specific speaker model obtained by modeling the feature of the voice of a specific speaker in the depressed state. For example, the second model generation unit  304  reflects the amount of displacement from the parameter included in the first unspecific speaker model to the parameter included in the second unspecific speaker model on the parameter included in the first specific speaker model. 
     The likelihood calculation unit  305  calculates the first likelihood as the likelihood of the first specific speaker model with respect to the feature of input voice and the second likelihood as the likelihood of the second specific speaker model with respect to the input voice. 
     The state determination unit  306  determinates the state of the speaker of the input voice based on the first likelihood and the second likelihood. 
     As described above, the state detection device  300  generates the first specific speaker model obtained by modeling the feature of the voice of a specific speaker in the undepressed state from the voice of the specific speaker in the undepressed state. Then, the state detection device  300  generates the second specific speaker model obtained by modeling the feature of the voice of the specific speaker in the depressed state from the first specific speaker model according to the correspondence information. 
     Thus, the state detection device  300  determinates the state of the input voice using the first specific speaker model specified for a specific speaker and the abnormal state model for a specific speaker generated from the first specific speaker model. As a result, the state detection device  300  can detect the state of the specific speaker with high accuracy. 
     In addition, since the state detection device  300  generates the second specific speaker model from the first specific speaker model according to the correspondence information, it may not be necessary to generate the second specific speaker model from the voice of the specific speaker in the depressed state. As a result, the state detection device  300  can suppress the load imposed at the enrolment of the voice of the user. 
     Other Embodiments 
       FIG. 4  is an explanatory view of a state detection device  400  according to an embodiment of the present invention. 
     The state detection device  400  includes storage units  410  through  440 , a static state model generation unit  450 , an abnormal state model generation unit  460 , a feature parameter extraction unit  470 , a likelihood calculation unit  480 , and a likelihood comparison unit  490 . 
     In  FIG. 4 , a device to be used is described for each of the processes for an “enrolment of a speaker” and “detection of the state of a speaker” for comprehensibility of the state detection device  100 . However, it is not to limit the configuration of the state detection device  400 , for example, the arrangement of the devices, the connection among the devices, etc. Also in  FIG. 4 , the storage units  410  through  440  are described as different storage units, but it is obvious that the storage units  410  through  440  may be realized as one storage unit. 
     The storage unit  410  is a storage device for storing a basic model generated in advance. The basic model is a model generated by quantizing the feature parameter extracted from among a large amount of utterance data acquired from unspecific speakers using the GMM. 
     In the present embodiment, non-linguistic information included in a voice waveform is used as a feature parameter. The non-linguistic information refers to the information other than the information needed for the recognition of a linguistic information included in a voice waveform. For example, the information relating to a pulse signal generated when a speaker utters his or her voice, the information relating to a change with time of a pulse signal generated when a speaker utters his or her voice, etc. may be used as a feature parameter. However, it is not the gist of the present embodiment to eliminate the use of the information needed for the recognition of a linguistic information included in a voice waveform. 
     As the information relating to the pulse signal generated when a speaker utters voice, the logarithm LPC residual power obtained from the LPC residual signal about the speech data of the speaker may be used. In addition, as the information relating to the change with time of the pulse signal generated when the speaker utters voice, the Δ logarithm LPC residual power obtained from the LPC residual signal about the speech data of the speaker may be used. 
     In the present embodiment, as a feature parameter, the logarithm LPC residual power and the Δ logarithm LPC residual power are used, but it is not to limit the feature parameter to the logarithm LPC residual power and the Δ logarithm LPC residual power. The feature parameter may be non-linguistic information included in the voice waveform of a speaker. 
     For example, as a feature parameter, the information about the intonation of speech may be used. It is to use the inclination that a speaker utters words with his or her intonation in a relaxed state, but he or she utters words with his or her intonation suppressed unconsciously when the speaker feels stressed. As the information relating to the intonation of a speech, a pitch frequency obtained from the speech data of a speech may be used. 
     Furthermore, the information about the intensity of voice may be used as a feature parameter. It is to use as a feature the inclination that the supply of energy to vocal cords decreases when the speaker feels stressed. The value indicating the flatness with respect to the LPC residual spectrum in a high frequency band, for example, a geometric average and an arithmetic average may be used as the information about the intensity of voice. 
     The storage unit  420  is a storage device for storing a static state model for a specific speaker indicating the speech features of a specific speaker in the static state. The storage unit  440  is a storage device for storing a abnormal state model for a specific speaker indicating the speech features in the abnormal state of a specific speaker. 
     The static state refers to a reference state for determination of the abnormal state. For example, the voice uttered by a user etc. of the state detection device  400  in the state which is determined as the static state may be used as the voice in the static state. Therefore, the voice uttered by a user etc. of the state detection device  400  in the state which is determined as different from the static state may be used as the voice in the abnormal state. 
     The storage unit  430  is a storage device for storing the correspondence table  431  indicating the correspondence between the static state model for unspecific speakers and the abnormal state model for unspecific speakers. In the correspondence table  431  according to the present embodiment, an average value and a variance are used in the parameters included in the GMM. Therefore, the information obtained by storing for each distribution the amount of movement of an average value and the amount of change of a variance when the static state model for unspecific speakers and the abnormal state model for unspecific speakers are compared with each other may be included in the correspondence table  431 . 
     The correspondence table  431  refers to the information about the amount of displacement from the static state model for unspecific speakers to the abnormal state model for unspecific speakers. Therefore, the abnormal state model for a specific speaker can be easily predicted from the static state model for a specific speaker using the amount of displacement indicated by the correspondence table  431 . 
     For example, when the average value of the distribution of the distribution numbers # 1 , # 2 , . . . in the static state model for a specific speaker is adjusted by μ 1 , μ 2 ′, . . . , and the variance of the distribution is adjusted by σ 1 ′ 2 , σ 2 ′ 2 , . . . , the abnormal state model for a specific speaker may be obtained. 
     In the present embodiment, the average value and the variance in the parameters included in the GMM are used in the correspondence table  431 , but the correspondence table  431  may also include, for example, the amount of change in weight of distribution which is one of the parameters included in the GMM. 
     (Enrolment of Speaker) 
     Described below is the outline of the operation of the state detection device  400  at the enrolment of a speaker. 
     For example, when the utterance data of the speaker F in the static state is input, the static state model generation unit  450  generates the static state model for a specific speaker about the speaker F by adapting the basic model to utterance data of the speaker F in the static state. Then, the static state model generation unit  450  stores the generated static state model for a specific speaker in the storage unit  420 . 
     Furthermore, the abnormal state model generation unit  460  generates the abnormal state model for a specific speaker about the speaker F by performing an adjustment of the average value and the variance in the parameters included in the static state model for a specific speaker about the speaker F based on the correspondence table  431 . Then, the abnormal state model generation unit  460  stores the generated abnormal state model for a specific speaker in the storage unit  440 . Thus, the enrolment of the speaker F is completed. 
     (Detection of State of Speaker) 
     Described below is the operation of the state detection device  400  when the state of a speaker is detected. 
     For example, when the utterance data of the speaker F is input, the feature parameter extraction unit  470  extracts a feature parameter from the input utterance data of the speaker F. Then, the likelihood calculation unit  480  reads the static state model for a specific speaker about the speaker F from the storage unit  420 . Then, the likelihood calculation unit  480  calculates the likelihood of the static state model for a specific speaker about the speaker F with respect to the feature parameter extracted by the feature parameter extraction unit  470 . Similarly, the likelihood calculation unit  480  reads the abnormal state model for a specific speaker about the speaker F from the storage unit  440 . Then, the likelihood calculation unit  480  calculates the likelihood of the abnormal state model for a specific speaker about the speaker F with respect to the feature parameter extracted by the feature parameter extraction unit  470 . 
     The likelihood comparison unit  490  compares the two likelihoods, which have been calculated by the likelihood calculation unit  480 , with each other, and determines the state of the speaker F, that is, whether the speaker F is in the static state or in the abnormal state. Then, the likelihood comparison unit  490  outputs the determination result to a specified device etc. Thus, the state detection of the speaker F is completed. 
       FIG. 5  is a practical example of the correspondence table  431 . 
     The correspondence table  431  includes the amount of movement of an average value and the amount of change in variance for each distribution included in the GMM. The correspondence table  431  illustrated in  FIG. 5  exemplifies the case in which the average value and the variance refer to two-dimensional values. However, the correspondence table  431  is not intended for limitation to the case in which the average value and the variance are two-dimensional. 
     The amount of movement of the average value refers to the amount of movement from the average value of the static state model for unspecific speakers to the average value of the abnormal state for unspecific speakers when the static state model for unspecific speakers is compared with the abnormal state model for unspecific speakers. The amount of change in variance refers to the amount of change from the variance of the static state model for unspecific speakers to the variance of the abnormal state for unspecific speakers when the static state model for unspecific speakers is compared with the abnormal state model for unspecific speakers. 
       FIG. 6  is an explanatory view of generating a static state model for a specific speaker and an abnormal state model for a specific speaker according to the present embodiment. The items (1) and (2) in  FIG. 6  correspond to the following items (1) and (2). The reference numerals # 1  through # 3  of the coordinates  601  and  602  indicate the distribution numbers. 
     (1) Generating a Static State Model for Specific Speaker 
     For example, assume that the basic model is expressed by the distribution enclosed by the dotted circle in the coordinates  601 . When the static state utterance data of the speaker F is input, the static state model generation unit  450  extracts a feature parameter from the static state utterance data of the speaker F. The feature vector included in the extracted feature parameter indicates the distribution as illustrated by the • in the coordinates  601 . The feature vector is described in detail as described later with reference to  FIG. 7 . 
     The static state model generation unit  450  estimates the static state model for a specific speaker about the speaker F by performing the Bayesian adaptation to the basic model using the feature parameter extracted from the static state utterance data of the speaker F. The Bayesian adaptation is well known by the non-patent document 3 etc. By the Bayesian adaptation, the distribution of the static state model for a specific speaker about the speaker F, for example, the distribution b enclosed by the circle of the solid line in the coordinates  601 , is estimated from the distribution a of the basic model. 
     (2) Generating an Abnormal State Model for Specific Speaker 
     When the estimation of the static state model for a specific speaker about the speaker F is completed, the abnormal state model generation unit  460  adjusts the average value and the variance among the parameters included in the GMM expressing the static state model for a specific speaker about the speaker F according to the correspondence table  431 . Thus, the distribution of the abnormal state model for a specific speaker about the speaker F, for example, the distribution c enclosed by the circle of the long and short dashed lines in the coordinates  602 , is estimated from the static state model for a specific speaker about the speaker F. 
     For example, when the distribution # 1  in the coordinates  602  is considered, the average value of the distribution b is adjusted in the in the direction of x 1  by −0.5 according to the correspondence table  431 , and adjusted in the in the direction of x 2  by 0.1. In addition, the variance of the distribution b is adjusted in the in the direction of x 1  by −0.02 according to the correspondence table  431 . As a result, the distribution such as the distribution c can be acquired from the distribution b. 
     In  FIG. 6 , the case in which the feature vector refers to a two-dimensional value is described for simple explanation, but it is not to limit the case to the two-dimensional feature vector. In addition, although only three distributions, that is, the distributions # 1  through # 3 , are illustrated in  FIG. 6 , it is only the exemplification, and is not to limit to the case in which three distributions are used. 
     (Practical Process at Enrolment of Speaker) 
       FIG. 7  is a flowchart of the process performed when a speaker is enrolled according to the present embodiment. 
     When the user performs a specified operation through an input device etc. provided for the state detection device  400 , the state detection device  400  starts the following processes (step S 700 ). 
     In step S 701 , the state detection device  400  digitizes the voice of the speaker acquired through the microphone etc. provided for the state detection device  400 , and generates utterance data. Then, the state detection device  400  stores the generated utterance data in the storage etc. 
     In step S 702 , the state detection device  400  extracts the feature parameter X from the utterance data generated in step S 701 . The feature parameter X may be extracted as follows according to, for example, section 1.2 of the non-patent document 1. 
     First, the state detection device  400  frames the utterance data generated in step S 701 . In the framing process, the state detection device  400  retrieves a signal for each section having a specified frame length of N from the signal sequence of utterance data. In this case, the state detection device  400  determines the next section by making a shift by the frame shift length T so that a certain section overlaps the next section by a specified period. For example, the value of about 20 through 40 ms may be used as the frame length of N, and the value of about 10 through 20 ms may be used as the frame shift length of T. 
     Next, the state detection device  400  calculates the product of the signal sequence s(n) of the framed utterance data and the weight called a analysis window by the following equation, thereby obtaining the signal sequence S W  (m; l). A hamming window w (l) may be used as an analysis window. 
     When the state detection device  400  performs an operation by the equation, for example, a DSP  1501  described later operates and changes the data stored in memory according to the program instruction including the algorithm depending on the equation, thereby generating a specified operation result. 
     
       
         
           
             
               
                 
                   
                     
                       
                         S 
                         w 
                       
                       ⁡ 
                       
                         ( 
                         
                           m 
                           ; 
                           l 
                         
                         ) 
                       
                     
                     = 
                     
                       
                         ∑ 
                         
                           m 
                           = 
                           0 
                         
                         
                           N 
                           - 
                           1 
                         
                       
                       ⁢ 
                       
                         
                           w 
                           ⁡ 
                           
                             ( 
                             m 
                             ) 
                           
                         
                         · 
                         
                           s 
                           ⁡ 
                           
                             ( 
                             
                               
                                 k 
                                 l 
                               
                               + 
                               m 
                             
                             ) 
                           
                         
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         
                           k 
                           0 
                         
                         = 
                         0 
                       
                       , 
                       
                         
                           k 
                           1 
                         
                         = 
                         T 
                       
                       , 
                       
                         
                           k 
                           2 
                         
                         = 
                         
                           2 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           T 
                         
                       
                       , 
                       … 
                       ⁢ 
                       
                           
                       
                       , 
                       
                         
                           k 
                           l 
                         
                         = 
                         lT 
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     where the hamming window w(n) may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       w 
                       ⁡ 
                       
                         ( 
                         n 
                         ) 
                       
                     
                     = 
                     
                       0.54 
                       - 
                       
                         0.46 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           cos 
                           ⁡ 
                           
                             ( 
                             
                               
                                 2 
                                 ⁢ 
                                 n 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 π 
                               
                               
                                 N 
                                 - 
                                 1 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         n 
                         = 
                         0 
                       
                       , 
                       1 
                       , 
                       … 
                       ⁢ 
                       
                           
                       
                       , 
                       
                         N 
                         - 
                         1 
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     In the equation (1) above, the subscript  1  corresponds to the position from which a signal is to be retrieved. Therefore, by increasing  1  at the intervals of the frame shift length of T, the signal sequence S W  (n) (n=0, 1, . . . , N−1) whose frame length of N is framed may be acquired. 
     Next, the state detection device  400  extracts the feature parameter from the framed signal sequence S W  (n). In the present embodiment, the parameter relating to the intensity of the pulse signal detected when a speaker utters voice and the change with time of the pulse signal detected when the speaker utters voice is extracted as a feature parameter. 
     Described below is the case in which the logarithm LPC residual power is used as the parameter relating to the intensity of the pulse signal detected when a speaker utters voice, and the Δ logarithm LPC residual power is used as the parameter relating to the change with time of the pulse signal detected when the speaker utters voice. The LPC residual signal may be obtained by using the descriptions in the section 1.2.2 in the non-patent document 1. 
     Assume that the linear predictive coefficient when the voice is regulated by a transfer function of all pole model is defined as a i  (i=1, 2, . . . , p). The linear predictive coefficient a i  may be obtained by a well-known method such as the Levinson-Durbin method. 
     When the calculation of the linear predictive coefficient a i  is completed, the state detection device  400  calculates the estimated value of S W  (n) from p prior samples. 
     
       
         
           
             
               
                 
                   
                     
                       
                         
                           S 
                           ^ 
                         
                         w 
                       
                       ⁡ 
                       
                         ( 
                         n 
                         ) 
                       
                     
                     = 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           0 
                         
                         p 
                       
                       ⁢ 
                       
                         
                           a 
                           i 
                         
                         · 
                         
                           
                             s 
                             w 
                           
                           ⁡ 
                           
                             ( 
                             
                               n 
                               - 
                               i 
                             
                             ) 
                           
                         
                       
                     
                   
                   ⁢ 
                   
                     
 
                   
                   ⁢ 
                   
                     ( 
                     
                       
                         n 
                         = 
                         0 
                       
                       , 
                       1 
                       , 
                       … 
                       ⁢ 
                       
                           
                       
                       , 
                       
                         N 
                         - 
                         1 
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     The LPC residual signal e (n) may be obtained by the difference between the estimated value obtained by the equation (3) and the S W  (n) obtained by the actually observed voice. Therefore, the state detection device  400  calculates the LPC residual signal e (n) by the following equation.
 
 e ( n )== s   w ( n )− Ŝ   w ( n ).  (4)
 
     The state detection device  400  calculates the logarithm power of the LPC residual signal in the frame l, that is, the logarithm LPC residual power in the frame l, by the following equation. 
     
       
         
           
             
               
                 
                   
                     power 
                     ⁡ 
                     
                       ( 
                       l 
                       ) 
                     
                   
                   = 
                   
                     
                       log 
                       10 
                     
                     ⁡ 
                     
                       ( 
                       
                         
                           ∑ 
                           
                             n 
                             = 
                             0 
                           
                           
                             N 
                             - 
                             1 
                           
                         
                         ⁢ 
                         
                           
                             e 
                             l 
                             2 
                           
                           ⁡ 
                           
                             ( 
                             n 
                             ) 
                           
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     The state detection device  400  calculates the Δ logarithm LPC residual power by the following equation. 
     
       
         
           
             
               
                 
                   
                     Δ 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       power 
                       ⁡ 
                       
                         ( 
                         l 
                         ) 
                       
                     
                   
                   = 
                   
                     
                        
                       
                         
                           ∑ 
                           
                             k 
                             = 
                             
                               - 
                               2 
                             
                           
                           2 
                         
                         ⁢ 
                         
                           k 
                           · 
                           
                             power 
                             ⁡ 
                             
                               ( 
                               
                                 l 
                                 + 
                                 k 
                               
                               ) 
                             
                           
                         
                       
                        
                     
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           
                             - 
                             2 
                           
                         
                         2 
                       
                       ⁢ 
                       
                         k 
                         2 
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     Δ is called a dynamic feature, and may be obtained as a regression coefficient of a change of logarithm power along the time axis. For example, in (1.21) of the section 1.3.1 of the non-patent document 1, an example of obtaining a cepstrum coefficient as a regression coefficient is described. The numerator in the right side is an absolute value to use the amount of change of logarithm LPC residual power for the feature parameter according to the present embodiment. 
     The two parameters obtained as described above, that is, the logarithm LPC residual power in the frame l and the Δ logarithm LPC residual power in the frame l, may be expressed using the two-dimensional vector X l . The vector X l  is called a “feature vector”. Furthermore, the entire sequence of the feature vector is referred to as a “feature parameter X”. 
     When the feature parameter X is completely extracted, the state detection device  400  passes control to step S 703 . 
     In Step  703 , the state detection device  400  generates a static state model for a specific speaker about the speaker F. The static state model for a specific speaker according to the present embodiment may be expressed using the GMM. Therefore, in step S 703 , the model parameter λ calm     —     user  included in the static state model for a specific speaker about the speaker F is obtained as described below. 
     In step S 702 , assume that T calm     —     user  frames are generated from the utterance data in the static state of the speaker F. In this case, the state detection device  400  calculates the number of frames in the sense of probability corresponding to the Bayesian adaptation with respect to the feature parameter obtained in step S 702 . λ gen  refers to a basic parameter of the GMM described later. In addition, K refers to the number of normal distributions included in the GMM. 
     
       
         
           
             
               
                 
                   
                     n 
                     
                       1 
                       , 
                       i 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         l 
                         = 
                         1 
                       
                       
                         T 
                         
                           calm 
                           ⁢ 
                           _ 
                           ⁢ 
                           user 
                         
                       
                     
                     ⁢ 
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           
                             i 
                             ❘ 
                             
                               x 
                               l 
                             
                           
                           , 
                           
                             λ 
                             gen 
                           
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     The state detection device  400  calculates the primary moment by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       E 
                       
                         1 
                         , 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       x 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         n 
                         
                           1 
                           , 
                           i 
                         
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         
                           T 
                           
                             calm 
                             ⁢ 
                             _ 
                             ⁢ 
                             user 
                           
                         
                       
                       ⁢ 
                       
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
                                 ❘ 
                                 
                                   x 
                                   l 
                                 
                               
                               , 
                               
                                 λ 
                                 gen 
                               
                             
                             ) 
                           
                         
                         · 
                         
                           x 
                           l 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   8 
                   ) 
                 
               
             
           
         
       
     
     The state detection device  400  calculates the secondary moment by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       E 
                       
                         1 
                         , 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       
                         x 
                         2 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         n 
                         
                           1 
                           , 
                           i 
                         
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         
                           T 
                           
                             calm 
                             ⁢ 
                             _ 
                             ⁢ 
                             user 
                           
                         
                       
                       ⁢ 
                       
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
                                 ❘ 
                                 
                                   x 
                                   l 
                                 
                               
                               , 
                               
                                 λ 
                                 gen 
                               
                             
                             ) 
                           
                         
                         · 
                         
                           x 
                           l 
                           2 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
           
         
       
     
     The data dependent adaptive coefficient α 1 ,i ρ , ρ={w, m, v} is obtained by the following equation using the parameter γ for adjustment of the level of the adaptation called the “Bayesian factor”. ρ is an index (subscript) indicating the weight (w), the average value (m), or the variance (v), but is not a numeric. 
     
       
         
           
             
               
                 
                   
                     α 
                     
                       1 
                       , 
                       i 
                     
                     ρ 
                   
                   = 
                   
                     
                       n 
                       
                         1 
                         , 
                         i 
                       
                     
                     
                       
                         n 
                         
                           1 
                           , 
                           i 
                         
                       
                       + 
                       
                         r 
                         ρ 
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
     
     Then, the state detection device  400  calculates the model parameter included in the GMM, that is, the weight p 1,i , average value μ 1,i , and the variance σ 1,i   2  of each Gaussian probabilistic density function included in the GMM, by the following equation. 
                     p     1   ,   i       =       {         α     1   ,   i     w     ·       n     1   ,   i         T     calm   ⁢   _   ⁢   user           +       (     1   -     α     1   ,   i     w       )     ·     p     0   ,   i           }     ·     γ   1               (   11   )               μ 1,i =α 1,i   m   ·E   1,i ( x )+(1−α 1,i   m )·μ 0,i   (12)
 
σ 1,i   2 =α 1,i   v   ·E   1,i ( x   2 )+(1−α 1,i   v )·(σ 0,i   2 +μ 0,i   2 )−μ 1,i   2   (13)
 
     γ 1  refers to a scale factor constant for amendment so that the total of the weights p 1,i  of all Gaussian probabilistic density functions included in the GMM may be 1.0. The scale factor constant γ 1  may be obtained by the following equation. 
     
       
         
           
             
               
                 
                   
                     γ 
                     1 
                   
                   = 
                   
                     1 
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                       ⁢ 
                       
                         p 
                         
                           1 
                           , 
                           k 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
     The state detection device  400  may calculate the model parameter λ calm     —     user  of the static state model for a specific speaker about the speaker F by performing only once the calculation above.
 
λ calm     —     user   ={p   1,k ,μ 1,k ,σ 1,k   2   }k= 1,2 , . . . ,K   (15)
 
     By the processes above, the static state model for a specific speaker about the speaker F is generated. 
     When the static state model for a specific speaker about the speaker F is completely generated, the state detection device  400  stores the model parameter λ calm     —     user  of the static state model for a specific speaker about the speaker F in the storage unit  420 . Then, the state detection device  400  passes control to step S 704 . 
     In step S 704 , the state detection device  400  generates the abnormal state model for a specific speaker about the speaker F by amending the model parameter λ calm     —     user  of the static state model for a specific speaker about the speaker F obtained in step S 703  according to the correspondence table  431 . The state detection device  400  calculates the average value and the variance ′μ 1,i   2  included in the model parameter λ abn     —     user  of the abnormal state model for a specific speaker about the speaker F by the following equation. In the present embodiment, the weight p 1,i  included in the model parameter λ calm     —     user  obtained in step S 703  is used for the weight ′p 1,i  included in the model parameter λ abn     —     user . 
     The state detection device  400  calculates the average value ′μ 1,i  by the following equation.
 
′μ 1,i =μ 1,i +μ i ′  (16)
 
     The state detection device  400  also calculates the variance ′σ 1,i   2  by the following equation (18) if the expression (17) holds.
 
σ 1,i   2 +σ i ′ 2 ≧β·σ 1,i   2   (17)
 
′σ 1,i   2 =σ 1,i   2 −σ i ′ 2   (18)
 
     The state detection device  400  also calculates the variance ′σ 1,i   2  by the following equation (20) if the expression (19) holds.
 
σ 1,i   2 +σ i ′ 2 &lt;β·σ 1,i   2   (19)
 
′σ 1,i   2 =β·σ 1,i   2   (20)
 
     where, for example, β may be set to 0.1 (β=0.1) because the width of the reduction of the variance of the abnormal state model for a specific speaker with respect to the static state model for a specific speaker is suppressed to 1/10 of the variance of the static state model for a specific speaker, thereby preventing the high probability of over-learning state on a specific value. 
     By the processes above, the state detection device  400  obtains the model parameter λ abn     —     user  of the abnormal state model for a specific speaker about the speaker F. Therefore, it is considered that the abnormal state model for a specific speaker about the speaker F has been generated.
 
λ abn     —     user   ={p   1,k ,′μ 1,k ,′σ 1,k   2   }k= 1,2 , . . . ,K   (21)
 
     In the present embodiment, the weight of the variance of the static state model for a specific speaker is used as is for the weight ′p i  of the distribution of the abnormal state model for a specific speaker. However, the weight ′p i  of the distribution of the abnormal state model for a specific speaker may be amended using an amount of change from the weight p 2,i  of the distribution of the static state model for unspecific speakers to the weight p 3,i  of the distribution of the abnormal state model for unspecific speakers as by the following equation. The weight p 2,i  of the distribution of the static state model for unspecific speakers and the weight p 3,i  of the distribution of the abnormal state model for unspecific speakers are described later with reference to  FIG. 11 .
 
′ p   i   =p   1,i   +p   i ′  (22)
 
     In this case, since a negative weight makes no sense, ′p i  is set to 0 (′p i =0) when ′p i ≦0. 
     When the processes above are completed, the state detection device  400  stores the model parameter λ abn     —     user  of the abnormal state model for a specific speaker about the speaker F in the storage unit  440 . Then, the state detection device  400  terminates the process performed at the enrolment of the speaker (step S 705 ). 
     (Practical Process when State of Speaker is Detected) 
       FIG. 8  is a flowchart of the process of detecting the state of a speaker according to the present embodiment. 
     In step S 801 , according to instructions of a user, the state detection device  400  digitizes the voice of the speaker F acquired through the microphone etc. provided for the state detection device  400 , and generates utterance data. Then, the state detection device  400  stores the generated utterance data in the storage etc. 
     In step S 802 , the state detection device  400  extracts the feature parameter X from the utterance data generated in step S 801 . The feature parameter X may be extracted by the same process as described above in step S 702  in  FIG. 7 . Therefore, the description of the extraction of the feature parameter X is omitted here. 
     In step S 803 , the state detection device  400  calculates the average logarithm likelihood L (X|λ calm     —     user ) of the feature parameter X extracted in step S 802  to the static state model for a specific speaker about the user F by the following equation. 
     
       
         
           
             
               
                 
                   
                     L 
                     ⁡ 
                     
                       ( 
                       
                         X 
                         ❘ 
                         
                           λ 
                           
                             calm 
                             ⁢ 
                             _ 
                             ⁢ 
                             user 
                           
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       T 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         T 
                       
                       ⁢ 
                       
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 x 
                                 i 
                               
                               ❘ 
                               
                                 λ 
                                 
                                   calm 
                                   ⁢ 
                                   _ 
                                   ⁢ 
                                   user 
                                 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   23 
                   ) 
                 
               
             
           
         
       
     
     In step S 804 , the state detection device  400  calculates the average logarithm likelihood L (X|λ abn     —     user ) of the feature parameter X extracted in step S 802  to the abnormal state model for a specific speaker about the user F by the following equation. 
     
       
         
           
             
               
                 
                   
                     L 
                     ⁡ 
                     
                       ( 
                       
                         X 
                         ❘ 
                         
                           λ 
                           
                             abn 
                             ⁢ 
                             _ 
                             ⁢ 
                             user 
                           
                         
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       T 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         T 
                       
                       ⁢ 
                       
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 x 
                                 i 
                               
                               ❘ 
                               
                                 λ 
                                 
                                   abn 
                                   ⁢ 
                                   _ 
                                   ⁢ 
                                   user 
                                 
                               
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   24 
                   ) 
                 
               
             
           
         
       
     
     In step S 805 , the state detection device  400  calculates the ratio of the average logarithm likelihood L (X|λ calm     —     user ) calculated in step S 803  to the average logarithm likelihood L (X|λ abn     —     user ) calculated in step S 804 , that is, the likelihood ratio Λ (X), by the following equation.
 
Λ( X )= L ( X|λ   calm     —     user )− L ( X|λ   abn     —     user )  (25)
 
     When the likelihood ratio Λ (X) calculated in step S 805  is smaller than the threshold TH 1  (NO in step S 806 ), the state detection device  400  determines that the speaker F is in the abnormal state (step S 807 ). In this case, the state detection device  400  outputs the determination result that the speaker F is in the abnormal state to a specified device. Furthermore, when the likelihood ratio Λ (X) calculated in step S 805  is equal to or exceeds the threshold TH 1  (YES in step S 806 ), the state detection device  400  determines that the speaker F is in the static state (step S 808 ). In this case, the state detection device  400  outputs the determination result that the speaker F is in the normal state to a specified device. 
     When the process above is terminated, the state detection device  400  terminates the process of detecting the state of the speaker (step S 809 ). 
     (Advance Preparation) 
     To detect the state of a speaker using the state detection device  400  according to the present embodiment, it may be necessary to make a specified advance preparation. In the advance preparation, the correspondence table  431  for use in state detection is generated. 
     The advance preparation may be performed by an information processing device  900  capable of communicating data with the state detection device  400 . In this case, the information processing device  900  may directly communicate data by connecting the devices through a network or a dedicated line, or may communicate data through a storage medium. Furthermore, the state detection device  200  itself may perform the advance preparation. 
     The advance preparation is described below with reference to  FIGS. 9 and 10 . 
       FIG. 9  is an explanatory view of the outline of the advance preparation according to the present embodiment.  FIG. 10  is an explanatory view of the outline of the process performed in the advance preparation illustrated in  FIG. 9 . The items (1) through (5) described below correspond to the items (1) through (5) described in  FIG. 9 . 
     (1) Clustering 
     In the advance preparation, learning data  901  prepared in advance is used to generate a basic model. A voice database used in generating an acoustic model for use in speech recognition may be used as the learning data  901 . The acoustic model may include the features extracted from the waveform data of various voices. 
     When the information processing device  900  is provided with the learning data  901 , it extracts a feature parameter from the learning data  901 . Then, the information processing device  900  performs the clustering on the extracted feature parameter, and divides the feature vectors included in the feature parameter into a plurality of clusters as illustrated by a in  FIG. 9 . 
     For the clustering, for example, a K-means method may be used as illustrated by (A) in  FIG. 10 . In the “a” in  FIGS. 9 and 10 , the mark x indicates a feature vector included in the feature parameter, and the mark ⋄ indicates a codebook vector. The broken lines indicate the boundary of the clusters. The feature vectors included in the feature parameter are divided into a plurality of clusters by the clustering by the K-means method. Each cluster includes a plurality of feature vectors centering the codebook vector. The “a” in  FIGS. 9 and 10  illustrates an example of dividing the feature vectors into three clusters for simple illumination, but obviously it is not to limit the number of divided clusters. 
     (2) Generating Basic Model 
     When the clustering of the feature parameter is completed, the information processing device  900  calculates the model parameter λ gen  of the GMM from the feature parameter divided into a plurality of clusters. The GMM specified by the calculated model parameter λ gen  is defined as a basic model. Practically, the following processes are performed. 
     First, the information processing device  900  calculates the model parameter λ init  from the feature parameter divided into a plurality of clusters. Then, for example, an initial GMM including the distributions b 1  through b 3  illustrated by (B 1 ) in  FIG. 10  is generated from the feature parameter illustrated by (A) in  FIG. 10 . 
     Then, the information processing device  900  updates the model parameter λ init  of the initial GMM until it converges on a specified value using the EM algorithm. The model parameter λ init  which has converged on a specified value is defined as the model parameter λ gen  of the basic model. As a result, for example, the basic model including the distributions c 1  through c 3  illustrated by (B 2 ) in  FIG. 10  is estimated from the initial GMM illustrated by (B 1 ) in  FIG. 10 . 
     (3) Model Adaptation 
     When the basic model is completely generated, the information processing device  900  adapts the basic model to a large number of pieces of prepared static state utterance data  902 , and calculates the model parameter λ calm  of the static state model for unspecific speakers. 
     In the present embodiment, the Bayesian adaptation is used in adapting a basic model to a large number of pieces of static state utterance data  902 . By the Bayesian adaptation using the feature parameter extracted from among the large number of pieces of static state utterance data  902 , a static state model for unspecific speakers including the distributions dl through d 3  illustrated by (C) in  FIG. 10  is estimated from, for example, the basic model illustrated by (B 2 ) in  FIG. 10 . The mark ◯ described in (C) in  FIG. 10  indicates the feature vectors included in the feature parameter extracted from the static state utterance data of a learning speaker set. 
     (4) Model Adaptation 
     When the static state model for unspecific speakers is completely generated, the information processing device  900  adapts the static state model for unspecific speakers to a large number of pieces of the prepared abnormal state utterance data  903 , thereby calculating a model parameter λ abn  of the abnormal state model for unspecific speakers. 
     In the present embodiment, the Bayesian adaptation is used in the process of adapting the static state model for unspecific speakers to the large number of pieces of abnormal state utterance data  903 . By the Bayesian adaptation using the feature parameter of the large number of pieces of abnormal state utterance data  903 , an abnormal state model for unspecific speakers including the distributions e 1  through e 3  illustrated by (D) in  FIG. 10  is estimated from, for example, the static state model for unspecific speakers illustrated by (C) in  FIG. 10 . The mark Δ described in (D) in  FIG. 10  indicates a feature vector included in the feature parameter extracted from the abnormal state utterance data of the learning speaker set. 
     (5) Calculation of Amount of Change 
     When the static state model for unspecific speakers and the abnormal state model for unspecific speakers are completely generated, the information processing device  900  calculates the amount of change between the static state model for unspecific speakers and the abnormal state model for unspecific speakers. In the present embodiment, both the static state model for unspecific speakers and the abnormal state model for unspecific speakers are expressed by the GMM. Then, according to the present embodiment, the information processing device  900  calculates the amount of change for each distribution about the average value and a variance in the model parameter included in the GMM. As a result of the calculation, the correspondence table  431  is acquired. 
     (Practical Process in Advance Preparation) 
       FIG. 11  is a flowchart of the process of an advance preparation according to the present embodiment. 
     The following process may be performed according to the descriptions in sections 2.1 and 2.2 of the non-patent document 2. 
     In step S 1101 , the information processing device  900  assigns an initial value to a cluster. Practically, the information processing device  900  determines at random the initial value of a codebook vector m i  (l) (i=1, 2, . . . , K) which regulates the center of the cluster, and generates the codebook vector m i  (l). 
     According to the present embodiment, K is set to 1024 (K=1024). In this case, the information processing device  900  selects at random  1024  vectors from among T feature vectors included in the feature parameter extracted from the learning data. Then, the information processing device  900  sets the selected  1024  feature vectors as the initial value of the codebook vector. However, it is not to limit the value of K to 1024 (K=1024). 
     In step S 1102 , the information processing device  900  assigns the feature vector to each cluster. Practically, the information processing device  900  assigns the feature vector other than the vector used as the initial value of the codebook vector m i  (l) among the T feature vectors included in the feature parameter extracted from the learning data to the codebook vector m i  (l) indicating the center of the closest cluster. The codebook vector m i  (l) indicating the center of the cluster closest to the feature vector X i  may be calculated by the following equation. 
     
       
         
           
             
               
                 
                   
                     index 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     centering 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     closest 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     cluster 
                   
                   = 
                   
                     arg 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       
                         min 
                         
                           1 
                           ≤ 
                           i 
                           ≤ 
                           K 
                         
                       
                       ⁢ 
                       
                          
                         
                           
                             x 
                             l 
                           
                           - 
                           
                             
                               m 
                               i 
                             
                             ⁡ 
                             
                               ( 
                               1 
                               ) 
                             
                           
                         
                          
                       
                     
                   
                 
               
               
                 
                   ( 
                   26 
                   ) 
                 
               
             
           
         
       
     
     In step S 1103 , the information processing device  900  calculates the center of gravity of the feature vectors assigned to the cluster in step S 1102 . Then, the information processing device  900  updates the codebook vector m i  (l) indicating the center of the cluster using the calculated center of gravity. 
     When the amount of update of the codebook vector m i  (l) in step S 1103  is equal to or exceeds the threshold, the information processing device  900  determines that the center of the cluster has changed (NO in step S 1104 ). In this case, the information processing device  900  passes control to step S 1102 . 
     When the amount of update of the codebook vector m i  (l) in step S 1103  is smaller than the threshold, the information processing device  900  determines that there is no change in the center of the cluster (YES in step S 1104 ). In this case, the information processing device  900  passes control to step S 1105 . 
     In step S 1105 , the information processing device  900  calculates the model parameter λ init  of the initial GMM from the feature vectors assigned to each cluster by the processes in steps S 1101  through S 1104 . 
     For example, the information processing device  900  calculates the average value μ i  of the feature vectors assigned to the cluster i. The information processing device  900  also calculates the variance σ i   2  of the feature vectors assigned to the cluster i. In addition, the information processing device  900  calculates the ratio of the number of feature vectors assigned to the cluster i to the total number of feature vectors in the T feature vectors calculated from the learning data. The calculated ratio is used as the weight p i  of the mixed distribution. 
     In step S 1106 , the information processing device  900  performs the following operation to calculate the model parameter λ gen  of the basic model. 
     The operation described below may be performed using the EM algorithm described in, for example, the non-patent document 3 etc. Although, for the consistency of the description according to the present embodiment, “i” is used as an index indicating the frame number in the non-patent document 3, “l” not “i” is used as the index indicating the frame number in the present embodiment. Then, in the present embodiment, “i” is used as the index indicating the cluster number. Although “M” is used in the non-patent document 3 as the number of mixtures, “K” is used as the number of mixtures in the present embodiment. In the non-patent document 3, “N” is used as the number of dimensions, “D” is used as the number of dimensions in the present embodiment. 
     For example, the GMM of the number of mixtures K with respect to the feature vector X l  of D dimensions in the frame l may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     p 
                     ⁡ 
                     
                       ( 
                       
                         
                           x 
                           l 
                         
                         ❘ 
                         λ 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       K 
                     
                     ⁢ 
                     
                       
                         p 
                         i 
                       
                       · 
                       
                         
                           b 
                           i 
                         
                         ⁡ 
                         
                           ( 
                           
                             x 
                             l 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   27 
                   ) 
                 
               
             
           
         
       
     
     where the probability density of the i-th Gaussian function to the D-dimensional feature parameter X l  may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       b 
                       i 
                     
                     ⁡ 
                     
                       ( 
                       
                         x 
                         l 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         
                           
                             2 
                             ⁢ 
                             π 
                           
                           
                             D 
                             / 
                             2 
                           
                         
                         ⁢ 
                         
                           
                              
                             
                               ∑ 
                               i 
                               
                                   
                               
                             
                              
                           
                         
                       
                     
                     ⁢ 
                     exp 
                     ⁢ 
                     
                       { 
                       
                         
                           - 
                           
                             1 
                             2 
                           
                         
                         ⁢ 
                         
                           
                             ( 
                             
                               
                                 x 
                                 l 
                               
                               - 
                               
                                 μ 
                                 i 
                               
                             
                             ) 
                           
                           ′ 
                         
                         ⁢ 
                         
                           
                             ∑ 
                             i 
                             
                               - 
                               1 
                             
                           
                           ⁢ 
                           
                             ( 
                             
                               
                                 x 
                                 l 
                               
                               - 
                               
                                 μ 
                                 i 
                               
                             
                             ) 
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   28 
                   ) 
                 
               
             
           
         
       
     
     In the equation (28) above, Σ indicates the covariance matrix of D×D. Assuming that only the diagonal components in the covariance matrix includes effective components, Σ may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     ∑ 
                     i 
                   
                   ⁢ 
                   
                     = 
                     
                       [ 
                       
                         
                           
                             
                               σ 
                               
                                 i 
                                 , 
                                 1 
                               
                               2 
                             
                           
                           
                             … 
                           
                           
                             0 
                           
                         
                         
                           
                             ⋮ 
                           
                           
                             ⋱ 
                           
                           
                             ⋮ 
                           
                         
                         
                           
                             0 
                           
                           
                             … 
                           
                           
                             
                               σ 
                               
                                 i 
                                 , 
                                 D 
                               
                               2 
                             
                           
                         
                       
                       ] 
                     
                   
                 
               
               
                 
                   ( 
                   29 
                   ) 
                 
               
             
           
         
       
     
     Therefore, the probability density of the i-th Gaussian function to the D-dimensional feature parameter X l  may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       b 
                       i 
                     
                     ⁡ 
                     
                       ( 
                       
                         x 
                         l 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         
                           
                             2 
                             ⁢ 
                             π 
                           
                           
                             D 
                             / 
                             2 
                           
                         
                         ⁢ 
                         
                           
                             ∏ 
                             
                               d 
                               = 
                               1 
                             
                             D 
                           
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           
                             σ 
                             
                               i 
                               , 
                               d 
                             
                           
                         
                       
                     
                     ⁢ 
                     exp 
                     ⁢ 
                     
                       { 
                       
                         - 
                         
                           
                             ∑ 
                             
                               d 
                               = 
                               1 
                             
                             D 
                           
                           ⁢ 
                           
                             
                               
                                 ( 
                                 
                                   
                                     x 
                                     
                                       l 
                                       , 
                                       d 
                                     
                                   
                                   - 
                                   
                                     μ 
                                     
                                       i 
                                       , 
                                       d 
                                     
                                   
                                 
                                 ) 
                               
                               2 
                             
                             
                               2 
                               ⁢ 
                               
                                   
                               
                               ⁢ 
                               
                                 σ 
                                 
                                   i 
                                   , 
                                   d 
                                 
                                 2 
                               
                             
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   30 
                   ) 
                 
               
             
           
         
       
     
     The model parameter λ may be expressed as follows.
 
λ={ p   i ,μ i ,σ i   2   }i= 1,2, . . . , K   (31)
 
     The information processing device  900  calculates the logarithm likelihood L 0  (X|λ init ) of the initial GMM for the feature parameter {X} including the T feature vectors by the following equation in which the model parameter λ init  of the initial GMM is assigned. 
     
       
         
           
             
               
                 
                   
                     L 
                     ⁡ 
                     
                       ( 
                       
                         X 
                         ❘ 
                         λ 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       T 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         T 
                       
                       ⁢ 
                       
                         log 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 x 
                                 l 
                               
                               ❘ 
                               λ 
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   32 
                   ) 
                 
               
             
           
         
       
     
     Next, the information processing device  900  calculates by the following equations the average value μ 0,i , the variance σ 0,i   2 , and the weight p 0,i  of the i-th Gaussian density function included in the GMM in the equation (28). 
     
       
         
           
             
               
                 
                   
                     μ 
                     
                       0 
                       , 
                       i 
                       , 
                       d 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         T 
                       
                       ⁢ 
                       
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
                                 ❘ 
                                 
                                   x 
                                   l 
                                 
                               
                               , 
                               λ 
                             
                             ) 
                           
                         
                         · 
                         
                           x 
                           
                             l 
                             , 
                             d 
                           
                         
                       
                     
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         T 
                       
                       ⁢ 
                       
                         p 
                         ⁡ 
                         
                           ( 
                           
                             
                               i 
                               ❘ 
                               
                                 x 
                                 l 
                               
                             
                             , 
                             λ 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   33 
                   ) 
                 
               
             
             
               
                 
                   
                     σ 
                     
                       0 
                       , 
                       i 
                       , 
                       d 
                     
                     2 
                   
                   = 
                   
                     
                       
                         
                           ∑ 
                           
                             l 
                             = 
                             1 
                           
                           T 
                         
                         ⁢ 
                         
                           
                             p 
                             ⁡ 
                             
                               ( 
                               
                                 
                                   i 
                                   ❘ 
                                   
                                     x 
                                     l 
                                   
                                 
                                 , 
                                 λ 
                               
                               ) 
                             
                           
                           · 
                           
                             x 
                             
                               l 
                               , 
                               d 
                             
                             2 
                           
                         
                       
                       
                         
                           ∑ 
                           
                             l 
                             = 
                             1 
                           
                           T 
                         
                         ⁢ 
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
                                 ❘ 
                                 
                                   x 
                                   l 
                                 
                               
                               , 
                               λ 
                             
                             ) 
                           
                         
                       
                     
                     - 
                     
                       μ 
                       
                         0 
                         , 
                         i 
                         , 
                         d 
                       
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   34 
                   ) 
                 
               
             
             
               
                 
                   
                     p 
                     
                       0 
                       , 
                       i 
                     
                   
                   = 
                   
                     
                       1 
                       T 
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         T 
                       
                       ⁢ 
                       
                         p 
                         ⁡ 
                         
                           ( 
                           
                             
                               i 
                               ❘ 
                               
                                 x 
                                 l 
                               
                             
                             , 
                             λ 
                           
                           ) 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   35 
                   ) 
                 
               
             
           
         
       
     
     where a posteriori probability for the i-th Gaussian function is given by the following equation. 
     
       
         
           
             
               
                 
                   
                     p 
                     ⁡ 
                     
                       ( 
                       
                         
                           i 
                           ❘ 
                           
                             x 
                             l 
                           
                         
                         , 
                         λ 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       
                         p 
                         i 
                       
                       · 
                       
                         
                           b 
                           i 
                         
                         ⁡ 
                         
                           ( 
                           
                             x 
                             l 
                           
                           ) 
                         
                       
                     
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                       ⁢ 
                       
                         
                           p 
                           k 
                         
                         · 
                         
                           
                             b 
                             k 
                           
                           ⁡ 
                           
                             ( 
                             
                               x 
                               l 
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   36 
                   ) 
                 
               
             
           
         
       
     
     When the calculation above is completed, the information processing device  900  calculates the logarithm likelihood L 1  (X|λ) of the GMM for the feature parameter {X} including the T feature vectors by the equation (33) in which the calculated model parameter λ={u 0,i , σ 0,i   2 , p 0,i } is assigned. 
     When the degree of the increase of the logarithm likelihood L 1  (X|λ) with respect to the previously calculated logarithm likelihood L 0  (X|λ init ) is equal to or exceeds the threshold, the information processing device  900  performs an operation by the equations (34) through (37), and calculates the model parameter λ. Similarly, the operation is performed by the equations (34) through (37) to calculate the model parameter λ until the degree of the increase of the logarithm likelihood L n+1  (X|λ) calculated the (n+1)th time with respect to the logarithm likelihood L n  (X|λ) calculated n-th time is smaller than the threshold. 
     In addition, when the degree of the increase of the logarithm likelihood L n+1  (X|λ) calculated the (n+1)th time with respect to the logarithm likelihood L n  (X|λ) calculated n-th time is smaller than the threshold, the information processing device  900  terminates the process of calculating the GMM by the EM algorithm. 
     In the process above, the information processing device  900  can calculate the model parameter λ gen  of the basic model. The model parameter λ gen  can be expressed by the following equation.
 
λ gen   ={p   0,i ,μ 0,i ,σ 0,i   2   }i= 1,2 , . . . ,K   (37)
 
     The basic model is estimated by the processes above. 
     When the model parameter λ gen  of the basic model is calculated, the information processing device  900  passes control to step S 1107 . 
     In step S 1107 , the information processing device  900  performs the following operation to calculate the model parameter λ calm  of the static state model for unspecific speakers by adapting the basic model to a large number of pieces of the static state utterance data  902 . 
     For the operations described below, the Bayesian adaptation disclosed by the patent document 4 etc. may be used. When the Bayesian adaptation is used, the correspondence between the distribution before the Bayesian adaptation and the distribution after the Bayesian adaptation may be easily grasped. For example, the amount of change after the Bayesian adaptation in the distribution of certain distribution numbers may be obtained by comparing the values of the distributions in the same distribution number. However, for consistency in the description of the present embodiment, the non-patent document 4 uses “T” for the index of the frame number, but the present embodiment uses “L” for the index of the frame number. Furthermore, although the non-patent document 4 expresses the weight of the i-th Gaussian density function by w i , the present embodiment expresses the weight of the i-th Gaussian density function by p i . Also, although the non-patent document 4 expresses the i-th Gaussian density function by p i  (x t ), the present embodiment expresses it by b i  (x l ). Furthermore, the non-patent document 4 expresses the i-th posteriori probability by Pr (i|x t ) while the present embodiment expresses it by p (i|x l , λ). 
     First, the information processing device  900  extract the feature parameter from among a large number of pieces of static state utterance data  902 . Then, the information processing device  900  performs the Bayesian adaptation on the T calm  feature vectors in the feature vectors included in the extracted feature parameter. 
     For example, the number of frames in the sense of probability in the Bayesian adaptation may be expressed by the following equation 
     
       
         
           
             
               
                 
                   
                     n 
                     
                       2 
                       , 
                       i 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         l 
                         = 
                         1 
                       
                       
                         T 
                         calm 
                       
                     
                     ⁢ 
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           
                             i 
                             ❘ 
                             
                               x 
                               l 
                             
                           
                           , 
                           
                             λ 
                             gen 
                           
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   38 
                   ) 
                 
               
             
           
         
       
     
     The primary moment may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       E 
                       
                         2 
                         , 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       x 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         n 
                         
                           2 
                           , 
                           i 
                         
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         
                           T 
                           calm 
                         
                       
                       ⁢ 
                       
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
                                 ❘ 
                                 
                                   x 
                                   l 
                                 
                               
                               , 
                               
                                 λ 
                                 gen 
                               
                             
                             ) 
                           
                         
                         · 
                         
                           x 
                           l 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   39 
                   ) 
                 
               
             
           
         
       
     
     In addition, the secondary moment may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       E 
                       
                         2 
                         , 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       
                         x 
                         2 
                       
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         n 
                         
                           2 
                           , 
                           i 
                         
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         
                           T 
                           calm 
                         
                       
                       ⁢ 
                       
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
                                 ❘ 
                                 
                                   x 
                                   l 
                                 
                               
                               , 
                               
                                 λ 
                                 gen 
                               
                             
                             ) 
                           
                         
                         · 
                         
                           x 
                           l 
                           2 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   40 
                   ) 
                 
               
             
           
         
       
     
     The data dependent adaptive coefficient α 2,i   ρ , ρ={w, m, v} may be expressed by the following equation using the parameter γ for adjustment of the degree of the adaptation called a “Bayesian factor”. The “ρ” is an index (subscript) indicating a weight (w), an average value (m), or a variance (v) of the Gaussian density function, and is not a numeric. 
     
       
         
           
             
               
                 
                   
                     α 
                     
                       2 
                       , 
                       i 
                     
                     ρ 
                   
                   = 
                   
                     
                       n 
                       
                         2 
                         , 
                         i 
                       
                     
                     
                       
                         n 
                         
                           2 
                           , 
                           i 
                         
                       
                       + 
                       
                         r 
                         ρ 
                       
                     
                   
                 
               
               
                 
                   ( 
                   41 
                   ) 
                 
               
             
           
         
       
     
     The information processing device  900  calculates the model parameters included in the GMM, that is, the weight p 2,i , the average value μ 2,i , and the variance σ 2,i   2 , by the following equation using the adaptive coefficient α 2,i   ρ  appearing in the equation (42). 
                     p     2   ,   i       =       {         α     2   ,   i     w     ·       n     2   ,   i         T   calm         +       (     1   -     α     2   ,   i     w       )     ·     p     0   ,   i           }     ·     γ   2               (   42   )               μ 2,i =α 2,i   m   ·E   2,i ( x )+(1−α 2,i   m )·μ 0,i   (43)
 
σ 2,i   2 =α 2,i   v   ·E   2,i ( x   2 )+(1−α 2,i   v )·(σ 0,i   2 +μ 0,i   2 )−μ 2,i   2   (44)
 
     “γ 2 ” is a scale factor constant for amendment to be made so that the sum of the weights p 2,i  of all Gaussian density functions included in the GMM may be 1.0. The scale factor constant γ 2  may be obtained by the following equation. 
     
       
         
           
             
               
                 
                   
                     γ 
                     2 
                   
                   = 
                   
                     1 
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                       ⁢ 
                       
                         p 
                         
                           2 
                           , 
                           i 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   45 
                   ) 
                 
               
             
           
         
       
     
     The information processing device  900  can obtain the model parameter λ calm  of the static state model for unspecific speakers by performing the operation only once by the equations (42) through (44) above. The model parameter λ calm  may be expressed by the following equation.
 
λ calm   ={p   2,k ,μ 2,k ,σ 2,k   2   }k= 1,2 , . . . ,K   (46)
 
     By the processes above, the static state model for unspecific speakers is estimated. 
     When the model parameter λ calm  of the static state model for unspecific speakers is calculated, the information processing device  900  passes control to step S 1108 . 
     In step S 1108 , the information processing device  900  performs the following operation to adapt the static state model for unspecific speakers to the large number of pieces of abnormal state utterance data  903 , thereby calculating the model parameter λ abn  of the abnormal state model for unspecific speakers. The operation described below may be performed by the Bayesian adaptation disclosed by the patent document 4 etc. as in step S 1107 . 
     First, the information processing device  900  extracts a feature parameter from among the large number of pieces of abnormal state utterance data  903 . Then, the information processing device  900  perform the Bayesian adaptation to the T abn  feature vectors in the feature vectors included in the extracted feature parameter. 
     For example, the number of frames in the sense of probability in the Bayesian adaptation may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     n 
                     
                       3 
                       , 
                       i 
                     
                   
                   = 
                   
                     
                       ∑ 
                       
                         l 
                         = 
                         1 
                       
                       
                         T 
                         abn 
                       
                     
                     ⁢ 
                     
                       p 
                       ⁡ 
                       
                         ( 
                         
                           
                             i 
                             ❘ 
                             
                               x 
                               l 
                             
                           
                           , 
                           
                             λ 
                             calm 
                           
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   47 
                   ) 
                 
               
             
           
         
       
     
     The primary moment may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
                       E 
                       
                         3 
                         , 
                         i 
                       
                     
                     ⁡ 
                     
                       ( 
                       x 
                       ) 
                     
                   
                   = 
                   
                     
                       1 
                       
                         n 
                         
                           3 
                           , 
                           i 
                         
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           l 
                           = 
                           1 
                         
                         
                           T 
                           abn 
                         
                       
                       ⁢ 
                       
                         
                           p 
                           ⁡ 
                           
                             ( 
                             
                               
                                 i 
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     The secondary moment may be expressed by the following equation. 
     
       
         
           
             
               
                 
                   
                     
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                   49 
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     The data dependent adaptive coefficient α 3,i   ρ , ρ={w, m, v} may be expressed by the following equation using the parameter γ for adjustment of the degree of the adaptation called a “Bayesian factor”. The “ρ” is an index (subscript) indicating a weight (w), an average value (m), or a variance (v) of the Gaussian density function, and is not a numeric. 
     
       
         
           
             
               
                 
                   
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                   ( 
                   50 
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     Then, the information processing device  900  calculates the model parameters included in the GMM, that is, the weight p 3,i , the average value μ 3,i , and the variance σ 3,i   2 , by the following equation using the adaptive coefficient α 3,i   ρ  appearing in the equation (50). 
                     p     3   ,   i       =       {         α     3   ,   i     w     ·       n     3   ,   i         T   abn         +       (     1   -     α     3   ,   i     w       )     ·     p     2   ,   i           }     ·     γ   3               (   51   )               μ 3,i =α 3,i   m   ·E   3,i ( x )+(1−α 3,i   m )·μ 2,i   (52)
 
σ 3,i   2 =α 3,i   v   ·E   3,i ( x   2 )+(1−α 3,i   v )·(σ 2,i   2 +μ 2,i   2 )−μ 3,i   2   (53)
 
     “γ 3 ” is a scale factor constant for amendment to be made so that the sum of the weights p 3,i  of all Gaussian density functions included in the GMM may be 1.0. The scale factor constant γ 3  may be obtained by the following equation. 
     
       
         
           
             
               
                 
                   
                     γ 
                     3 
                   
                   = 
                   
                     1 
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                       ⁢ 
                       
                         p 
                         
                           3 
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                   ( 
                   54 
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     The information processing device  900  may obtain the model parameter λ abn  of the abnormal state model for unspecific speakers by performing the operation by the equations (51) through (53) above. The model parameter λ abn  may be expressed by the following equation.
 
λ abn   ={p   3,k ,μ 3,k σ 3,k   2   }k= 1,2 , . . . ,K   (55)
 
     By the processes above, the abnormal state model for unspecific speakers is estimated. 
     When the model parameter λ abn  of the abnormal state model for unspecific speakers is calculated, the information processing device  900  passes control to step S 1109 . 
     In step S 1109 , the information processing device  900  performs the following operations on all distributions, and calculates the difference between the model parameter λ calm  calculated in step S 1107  and the model parameter λ abn  calculated in step S 1108 . 
     The information processing device  900  calculates by the following equation the amount of change in the distribution i from the average value included in the model parameter λ calm  of the static state model for unspecific speakers to the average value included in the model parameter λ abn  of the abnormal state model for unspecific speakers.
 
μ i ′=μ 3,i −μ 2,i   (56)
 
     The information processing device  900  calculates by the following equation the amount of change in the distribution i from the variance included in the model parameter λ calm  of the static state model for unspecific speakers to the variance included in the model parameter λ abn  of the abnormal state model for unspecific speakers.
 
σ i ′ 2 =σ 3,i   2 −σ 2,i   2   (57)
 
     When the operations above are completed, the information processing device  900  stores the operation result as the correspondence table  431  in the storage unit etc. provided in the information processing device  900 . Then, the information processing device  900  terminates the process for the advance preparation (step S 1110 ). 
     In the present embodiment, the average value and the variance included in the model parameter are used in the correspondence table  431 , but the weight of the distribution included in the model parameter may also be used. In this case, the information processing device  900  calculates the amount of change of the weight of the distribution in the distribution i by the following equation.
 
 p   i   ′=p   3,i   −p   2,i   (58)
 
     Other Embodiment 
       FIG. 12  is an explanatory view of the state detection device  1200  according to the present embodiment. 
     The state detection device  1200  illustrated in  FIG. 12  includes the storage units  410  through  440 , the static state model generation unit  450 , and the abnormal state model generation unit  460 , but is omitted for simplicity of the figure. 
     The state detection device  1200  includes a ADC (analog digital converter)  1202 , an speech data storing unit  1203 , an speech data read unit  1204 , the likelihood calculation unit  480 , and the likelihood comparison unit  490 . The state detection device  1200  also includes an update determination unit  1205  and an update data generation unit  1206 . The state detection device  1200  further includes a ring buffer  1207  for update of a static state model and a ring buffer  1208  for update of an abnormal state model. Furthermore, the state detection device  1200  includes an update process control unit  1209 , a static state model update unit  1210 , an abnormal state model generation unit  1211 , and an abnormal state model update unit  1212 . 
     The ADC  1202  converts the speech signal of the speaker acquired through a microphone  1201  from an analog signal to a digital signal, and outputs the converted speech data to the audio storing unit  1203 . Upon receipt of the speech data from the ADC  1202 , the audio storing unit  1203  stores the received speech data in a storage medium  1213 . The storage medium  1213  may use various storage media such as USB memory, an SD card, etc. 
     Upon receipt of an instruction to update a model, the speech data read unit  1204  reads the speech data stored in the storage medium  1213 , and outputs the read speech data to the likelihood calculation unit  480 . 
     The likelihood calculation unit  480  calculates the likelihood of the speech data received from the speech data read unit  1204  with the static state model for a specific speaker and with the abnormal state model for a specific speaker. Then, the likelihood calculation unit  480  outputs the calculated likelihood to the likelihood comparison unit  490  and the update determination unit  1205 . 
     The likelihood comparison unit  490  determines the state of the speaker who has uttered the voice input to the microphone  1201 , for example, whether the speaker is in the static state or in the abnormal state, based on the two likelihoods calculated by the likelihood calculation unit  480 . Then, the likelihood comparison unit  490  outputs the determination result to a specified device etc. 
     The update determination unit  1205  determines whether or not the static state model for a specific speaker or the abnormal state model for a specific speaker is updated depending on the likelihood received from the likelihood calculation unit  480 . In the present embodiment, when the static state model for a specific speaker is to be updated, the abnormal state model for a specific speaker is simultaneously updated, but only the static state model for a specific speaker may be updated. The update determination unit  1205  outputs the determination result to the update data generation unit  1206 . 
     The update data generation unit  1206  outputs the determination result received from the update determination unit  1205  to the update process control unit  1209 . When the determination result received from the update determination unit  1205  is the update of the static state model for a specific speaker, the update data generation unit  1206  extracts a feature parameter from the speech data read from the storage medium  1213 . Then, the update data generation unit  1206  stores the extracted feature parameter in the ring buffer  1207  for update of a static state model. 
     In addition, when the determination result received from the update determination unit  1205  is the update of the abnormal state model for a specific speaker, the update data generation unit  1206  extracts the feature parameter from the speech data read from the storage medium  1213 . Then, the update data generation unit  1206  stores the extracted feature parameter in the ring buffer  1208  for update of an abnormal state model. 
     The ring buffer  1207  for update of a static state model and the ring buffer  1208  for update of an abnormal state model are ring buffers provided with a specified storage capacity. When the storage capacity is fully used, the ring buffer  1207  for update of a static state model and the ring buffer  1208  for update of an abnormal state model overwrite new data in order from the area storing older data. 
     The update process control unit  1209  instructs the static state model update unit  1210  or the abnormal state model update unit  1212  to perform the update process based on the determination result received from the update data generation unit  1206 . 
     For example, if the determination result received from the update determination unit  1205  refers to the update of a static state model for a specific speaker, the update process control unit  1209  instructs the static state model update unit  1210  to update a static state model for a specific speaker. If the determination result received from the update determination unit  1205  refers to the update of an abnormal state model for a specific speaker, the update process control unit  1209  instructs the abnormal state model update unit  1212  to update an abnormal static state model for the specific speaker. 
     The static state model update unit  1210  reads a feature parameter from the ring buffer  1207  for update of a static state model. Then, the static state model update unit  1210  estimates a new static state model for a specific speaker from the static state model for a specific speaker stored in the storage unit  420  by the Bayesian adaptation using the read feature parameter. Then, the static state model update unit  1210  updates the static state model for a specific speaker stored in the storage unit  420  to the newly estimated static state model for a specific speaker. 
     According to the present embodiment, a new static state model for a specific speaker is estimated from the static state model for a specific speaker stored in the storage unit  420 , but the new static state model for a specific speaker may be estimated from a basic model. 
     The abnormal state model generation unit  1211  adjusts the model parameter of the static state model for a specific speaker estimated by the static state model update unit  1210  according to the correspondence table  431  and generates a new abnormal state model for a specific speaker. Then, the abnormal state model generation unit  1211  updates the abnormal state model for a specific speaker stored in the storage unit  440  to the newly generated abnormal state model for a specific speaker. 
     The abnormal state model update unit  1212  reads a feature parameter from the ring buffer  1208  for update of an abnormal state model. Then, the abnormal state model update unit  1212  estimates a new abnormal state model for a specific speaker from the abnormal state model for a specific speaker stored in the storage unit  440  by the Bayesian adaptation using the read feature parameter. The abnormal state model update unit  1212  updates the abnormal state model for a specific speaker stored in the storage unit  440  to the newly estimated abnormal state model for a specific speaker. 
     In the present embodiment, a new abnormal state model for a specific speaker is estimated from the abnormal state model for a specific speaker stored in the storage unit  440 , but a new abnormal state model for a specific speaker may be estimated from a basic model. 
       FIGS. 13 and 14  are flowcharts of the operation of the state detection device  1200  according to the present embodiment. 
     When the voice of the speaker F is input through the microphone  1201 , the state detection device  1200  starts the following process (step S 1300 ). 
     In step S 1301 , the state detection device  1200  converts the input voice of the speaker F to the digital data. Then, the state detection device  1200  stores the digitized speech data of the speaker F in the storage medium  1213 . 
     The state detection device  1200  receives an instruction to update a model from an input unit provided for the state detection device  1200  or the CPU (central processing unit) etc. provided for a device including the state detection device  1200 , for example, a mobile telephone etc. (step S 1302 ). In this case, the state detection device  1200  reads the speech data of the speaker F from the storage medium  1213  (step S 1303 ). 
     The state detection device  1200  performs the processes in steps S 1304  through S 1310 , and calculates the likelihood ratio Λ (X). The processes in steps S 1304  through S 1310  are similar to those in steps S 802  through S 808  in  FIG. 8 , and the detailed description is omitted here. 
     When the absolute value |Λ (X)| of the likelihood ratio Λ (X) is smaller than the threshold TH 2  (NO in step S 1311 ), the state detection device  1200  terminates the process (step S 1318 ). The threshold TH 2  uses the value with which the likelihood ratio Λ clearly indicates the static state or the abnormal state. 
     When the absolute value |Λ (X)| of the likelihood ratio Λ (X) is equal to or exceeds the threshold TH 2  (YES in step S 1311 ), the state detection device  1200  determines that the speaker F is clearly in the static state or the abnormal state. Therefore, the state detection device  1200  determines that the model may be updated, and passes control to step S 1312 . 
     Then, when the likelihood ratio Λ (X) is equal to or exceeds the threshold TH 1  (YES in step S 1312 ), the state detection device  1200  determines that the speaker F is clearly in the static state, the static state model for a specific speaker is to be updated, and control is passed to step S 1313 . In this case, the state detection device  1200  extracts a feature parameter from the speech data stored in the storage medium  1213 . Then, the state detection device  1200  writes the extracted feature parameter to the ring buffer  1207  for update of a static state model (step S 1313 ). 
     In step S 1314 , the state detection device  1200  reads the feature parameter from the ring buffer  1207  for update of a static state model. Then, the state detection device  1200  estimates a new static state model for a specific speaker from the static state model for a specific speaker stored in the storage unit  420  by the Bayesian adaptation using the read feature parameter. The estimating process may be realized by the state detection device  1200  executing the process similar to the process in step S 703  in  FIG. 7 . The state detection device  1200  updates the static state model for a specific speaker stored in the storage unit  420  to the newly estimated static state model for a specific speaker. 
     In step S 1315 , the state detection device  1200  adjusts the model parameter of the static state model for a specific speaker updated in step S 1314  according to the correspondence table  431 , and generates a new abnormal state model for a specific speaker. The generating process may be realized by the state detection device  1200  executing the process similar to the process in step S 704  in  FIG. 7 . The state detection device  1200  updates the abnormal state model for a specific speaker stored in the storage unit  440  to the newly generated abnormal state model for a specific speaker. Then, the state detection device  1200  terminates the process (step S 1318 ). 
     On the other hand, when the likelihood ratio Λ (X) is smaller than the threshold TH 1  (NO in step S 1312 ), the state detection device  1200  determines that the speaker F is clearly in the abnormal state, the abnormal state model for a specific speaker is to be updated, and passes control to step S 1316 . In this case, the state detection device  1200  extracts a feature parameter from the speech data stored in the storage medium  1213 . Then, the state detection device  1200  writes the extracted feature parameter to the ring buffer  1208  for update of an abnormal state model (step S 1316 ). 
     In step S 1317 , the state detection device  1200  reads the feature parameter from the ring buffer  1208  for update of an abnormal state model. Then, the state detection device  1200  estimates a new abnormal state model for a specific speaker from the abnormal state model for a specific speaker stored in the storage unit  440  by the Bayesian adaptation using the read feature parameter. The estimating process may be realized by the state detection device  1200  performing the process similar to the process in step S 703  in  FIG. 7 . The state detection device  1200  updates the abnormal state model for a specific speaker stored in the storage unit  440  to the newly estimated abnormal state model for a specific speaker. Then, the state detection device  1200  terminates the process (step S 1318 ). 
       FIG. 15  is an example of the configuration of the state detection device described with reference to the embodiment above. The configuration in  FIG. 15  may be applied to any of the state detection device  300 , the state detection device  400 , and the state detection device  1200 . Described below is the state detection device  400  as an example. 
     The state detection device illustrated in  FIG. 15  includes a DSP  1501 , RAM  1502 , flash memory  1503 , an ADC  1504 , storage  1505 , and an I/O unit  1506 . These devices are connected to a bus for communication of data with one another. 
     The DSP  1501  is an arithmetic device for reading a program etc. which realizes the state detection according to the present embodiment from the RAM  1502 , and executing it. For example, the static state model generation unit  450 , the abnormal state model generation unit  460 , the feature parameter extraction unit  470 , the likelihood calculation unit  480 , the likelihood comparison unit  490 , etc. illustrated in  FIG. 4  may be realized by allowing the DSP  1501  to execute a specified program. 
     In addition, for example, the speech data storing unit  1203 , the speech data read unit  1204 , the update determination unit  1205 , the update data generation unit  1206 , the update process control unit  1209 , the static state model update unit  1210 , the abnormal state model generation unit  1211 , the abnormal state model update unit  1212 , etc. illustrated in  FIG. 12  may be realized by allowing the DSP  1501  to execute a specified program. 
     The RAM  1502  is a volatile storage device used for executing a program for realizing the state detection according to the present embodiment. 
     The flash memory  1503  is a non-volatile storage device for storing a program realizing the detection of the state according to the present embodiment. 
     The ADC  1504  is a device for converting an analog signal into a digital signal. For example, the ADC  1504  generates speech data by digitizing an analog signal such as speech signals etc. 
     The storage  1505  is a non-volatile storage device storing a large amount of data, for example, speech data etc. The storage  1505  may be, for example, a magnetic disk storage device etc. The storage  1505  may be used as the storage units  410  through  440 , the ring buffer  1207  for update of a static state model, the ring buffer  1208  for update of an abnormal state model, etc. 
     The I/O unit  1506  controls the input/output of data with an external device. For example, the result of the state detection according to the present embodiment is output to the specified device connected to the state detection device  400 . 
     A readable storage medium to an information processing device such as the RAM  1502 , the flash memory  1503 , the storage  1505 , etc. may be used as a non-transitory medium. 
       FIG. 16  is an example of the configuration of the mobile telephone  1600  including a state detection device described above with reference to the embodiments above. 
     The mobile telephone  1600  includes a state detection device  1601 , a control unit  1602 , a communication control unit  1603 , a speaker  1604 , a microphone  1605 , and a display unit  1606 . 
     The state detection device  1601  may be the state detection device  300 , the state detection device  400 , or the state detection device  1200 . 
     The control unit  1602  controls the entire mobile telephone  1600 . For example, the control unit  1602  digitizes the voice input from the microphone  1605  and output to the communication control unit  1603 . In addition, the control unit  1602  outputs the speech data transmitted from the communication control unit  1603  to the  1605 . 
     In addition, the control unit  1602  outputs the speech data transmitted from the microphone  1605  to the state detection device  1601 . Then, the control unit  1602  displays the determination result output from the state detection device  1601  on the display unit  1606 . 
     The control unit  1602  outputs the speech data transmitted from the communication control unit  1603  to the state detection device  1601 . Then, the control unit  1602  instructs the state detection device  1601  to enrol a speaker and update a model. 
     The communication control unit  1603  controls the communication in accordance with a specified protocol, and transmits the speech data received from the control unit  1602  to the mobile telephone as a destination. The communication control unit  1603  outputs the speech data transmitted from the mobile telephone as the destination to the control unit  1602 . 
     In the state detection device described with reference to the embodiments above, the storage unit  410  may be an example of a basic model storage unit. 
     When the undepressed state is a static state, it is considered that the depressed state is an abnormal state. In this case, the static state model for unspecific speakers may be an example of the first unspecific speaker model. The abnormal state model for unspecific speakers may be an example of a second unspecific speaker model. The static state model of the speaker F may be an example of a first specific speaker model. The static state model generation unit  450  may be an example of a first model generation unit. The abnormal state model of the speaker F may be an example of a second specific speaker model. The abnormal state model generation unit  460  may be an example of a second model generation unit. 
     The correspondence table  431  may be an example of correspondence information. The storage unit  430  may be an example of a correspondence information storage unit. The likelihood calculation unit  480  may be an example of a likelihood calculation unit. The likelihood comparison unit  490  may be an example of a state determination unit. 
     The update determination unit  1205  may be an example of an update determination unit. The components including the update data generation unit  1206 , the update process control unit  1209 , the static state model update unit  1210 , the abnormal state model generation unit  1211 , and the abnormal state model update unit  1212  may be an example of a model update unit. 
     As described above, the state detection device  400  estimates a static state model for a specific speaker about the specific speaker F from the static state utterance data of the specific speaker, for example, the speaker F. Then, the state detection device  400  estimates a abnormal state model for a specific speaker about the specific speaker F by adjusting the parameter included in the static state model for a specific speaker about the specific speaker F according to the correspondence table  431 . 
     Thus, the state detection device  400  detects a state using the static state model for a specific speaker specialized for the specific speaker F generated from the static state utterance data of the specific speaker F, and the abnormal state model for a specific speaker estimated from the static state model for a specific speaker. Therefore, the state detection device  400  can detect the state about the specific speaker F with high accuracy. 
     In addition, the state detection device  400  estimates a abnormal state model for a specific speaker about the specific speaker F from the static state model for a specific speaker about the specific speaker F. Therefore, the state detection device  400  can be free of a process of a heavy load in which a abnormal state model for a specific speaker about the specific speaker F is estimated from the abnormal state utterance data about the specific speaker F. As a result, the state detection device  400  can suppress the load imposed during the enrolment of a speaker. 
     In estimating the static state model for a specific speaker and the abnormal state model for a specific speaker about the specific speaker F, only the static state utterance data of the specific speaker F is used. Therefore, the user detects the state using the state detection device  400  if only the static state utterance data about the specific speaker F is enrolled in the state detection device  400 . As a result, the state detection device  400  can allow the user to easily perform state detection. Since the user may enrol the utterance data of the specific speaker F in the static state which can be easily acquired, not the utterance data of the specific speaker F in the abnormal state which may not be easily acquired, the state detection device  400  can allow the user to easily perform state detection. 
     When the speech data which clearly indicates the static state or the abnormal state is input, the state detection device  1200  uses the speech data and updates the static state model for a specific speaker or the abnormal state model for a specific speaker. As a result, the state detection device  1200  can detect the state of a specific speaker F with higher accuracy. 
     As described above, according to the disclosed state detection device, the state of the specific speaker can be detected with high accuracy with the load suppressed on the state detection device. 
     The procedure of the process illustrated in the flowcharts in  FIGS. 7 ,  8 ,  11 ,  13 , and  14  is not intended for limiting the order of the processes. Therefore, it is obvious that the order of the processes may be changed if necessary. 
     All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.