Patent Publication Number: US-2023141724-A1

Title: Emotion recognition system and emotion recognition method

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application claims priority from Japanese application JP2021-183862, filed on Nov. 11, 2021, the contents of which is hereby incorporated by reference into this application. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention generally relates to a technique for inferring emotions expressed in speech. 
     2. Description of the Related Art 
     Emotion information included in human speech plays an important role in human communication. There is a possibility that whether a purpose of communication has been achieved can be determined by movement of emotions, which creates a demand for analyzing emotions in communication. In business situations such as daily sales activities and reception operations at a call center, it is necessary to analyze emotions in a lot of speech-based communication. Thus, speech emotion recognition by a machine is desired. 
     The speech emotion recognition outputs categories of emotions included in input speech or a degree for each emotion category for the speech. As a mechanism thereof, there are a method of performing classification or regression analysis from features of speech signals in accordance with a predetermined rule, and a method of obtaining such a rule by machine learning. 
     In recent years, a speech interaction apparatus capable of easily performing emotion recognition according to a user has been disclosed (see JP 2018-132623 A). 
     SUMMARY OF THE INVENTION 
     In a rule-based method, a result that is output on the basis of one piece of speech data depends on a rule or a threshold set by a developer. In a machine learning method, a parameter is determined on the basis of speech data collected for training and a labeling result by a person (labeler) who labels the speech data with emotional impressions received by listening to that speech data as correct values. However, in any case, determination is made depending on subjectivity of the minority such as a developer or a labeler, so that an output of an emotion recognition device may deviate from an actual user impression. 
     The present invention has been made in view of the above points, and an object of the present invention is to propose an emotion recognition system and the like capable of appropriately recognizing emotions expressed in speech. 
     In order to solve such a problem, the present invention includes an input unit configured to input first speech data and second speech data, and a processing unit configured to input the first speech data and the second speech data to a differential-emotion recognition model that infers a differential emotion between two pieces of speech data, and acquire, from the differential-emotion recognition model, differential-emotion information indicating a differential emotion between the first speech data and the second speech data. 
     In the above configuration, for example, since the differential emotion (that is, relative emotion) is inferred from the two pieces of speech data, an emotion recognized by the emotion recognition system can be closer to an actual user impression than an emotion inferred from one piece of speech data (that is, absolute emotion). 
     According to the present invention, it is possible to implement an emotion recognition system that recognizes emotions expressed in speech with high accuracy. Problems, configurations, and advantageous effects other than those described above will be clarified by the following description of embodiments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram illustrating an example of a processing flow of a differential-emotion recognition apparatus according to a first embodiment; 
         FIG.  2    is a diagram illustrating an example of training speech data according to the first embodiment; 
         FIG.  3    is a diagram illustrating an example of training differential-emotion label data according to the first embodiment; 
         FIG.  4    is a diagram illustrating an example of a configuration of a differential-emotion recognition apparatus according to the first embodiment; 
         FIG.  5    is a diagram illustrating an example of a flowchart of a training program according to the first embodiment; 
         FIG.  6    is a diagram illustrating an example of a flowchart of a differential-emotion recognition program according to the first embodiment; 
         FIG.  7    is a diagram illustrating an example of emotion transition data according to the first embodiment; 
         FIG.  8    is a diagram illustrating an example of a user interface according to the first embodiment; and 
         FIG.  9    is a diagram illustrating an example of a processing flow of a differential-emotion recognition apparatus according to a second embodiment. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     (I) First Embodiment 
     Hereinafter, embodiments of the present invention will be described in detail. However, the present invention is not limited to the embodiments. 
     In a conventional technique, there is a possibility that an unintended result is output because a rule functions incorrectly due to influence of a variable factor of speaker characteristics in input speech, a variable factor of environmental characteristics in input speech, and the like. Recently, the advent of deep learning allows for handling more complicated rules in machine learning, and efforts have been widely made to solve this problem. Although the accuracy has been improved, it is difficult to say that the problem has been sufficiently solved. 
     In this regard, an emotion recognition system according to the present embodiment compares speech data within an individual rather than between individuals to recognize speech emotions. The speech emotions are the inside of a person expressed as a voice, including delight, anger, sorrow, and pleasure, negativeness or positiveness, and the like. The emotion recognition system uses an emotion label given by listening to two speech patterns of a certain person (a label of differential assessment (relative assessment) that quantifies emotions grasped from the two speech patterns of the person) rather than an emotion label given by listening to a single speech pattern of a certain person (a label of absolute assessment that quantifies current emotions, for example, the person is happy or sad). Thus, the labeling work is easy for labelers, and the label of the differential assessment is more reliable than the label of the absolute assessment. The two speech patterns used by the emotion recognition system to infer emotions come from the same speaker, but may be discontinuous. However, it is desirable to use speech patterns acquired on the same day and/or at the same place. 
     According to the emotion recognition system, it is possible to recognize speech emotions based on a difference between speech patterns of the same person while suppressing the influence of the speaker characteristics and the environmental characteristics as compared with the related art. 
     Next, the embodiments of the present invention will be described with reference to the drawings. The following description and drawings are examples for explaining the present invention, and some parts are omitted and simplified as appropriate for the sake of clarity of explanation. The present invention can also be implemented in various other forms. Unless otherwise specified, each constituent element may be singular or plural. In the following description, the same elements are denoted by the same reference numerals in the drawings, and the description thereof will be omitted as appropriate. 
     Expressions such as “first”, “second”, and “third” in the present specification are used to identify constituent elements, and do not necessarily limit the number or order of the constituent elements. In addition, reference numerals for identifying constituent elements are used in each context, and a reference numeral used in one context does not necessarily indicate the same configuration in another context. Furthermore, a constituent element identified by a certain reference numeral is not hindered from concurrently serving a function of a constituent element identified by another reference numeral. 
       FIG.  1    is a diagram illustrating an example of a processing flow of a differential-emotion recognition apparatus  101  according to the present embodiment. 
     First, in a training phase  110 , a user  102  prepares training speech data  111  and training differential-emotion label data  112 . Next, the user  102  uses the differential-emotion recognition apparatus  101  to generate a differential-emotion recognition model  113  by training. 
     Next, in an inference phase  120 , the user  102  inputs continuous speech data  121  to the differential-emotion recognition apparatus  101  and acquires emotion transition data  122 . 
       FIG.  2    is a diagram illustrating an example (training speech table  200 ) of the training speech data  111 . 
     The training speech table  200  stores a plurality of speech waveforms (speech data). Each of the plurality of speech waveforms is given a speech ID and a speaker ID. The speech ID represents a code for uniquely identifying a speech waveform. The speaker ID, which is assigned to a speaker of a speech waveform, represents a code for uniquely identifying a speaker. For that matter, the training speech table  200  stores a plurality of speech waveforms of a plurality of persons. 
       FIG.  3    is a diagram illustrating an example (training differential-emotion label table  300 ) of the training differential-emotion label data  112 . 
     The training differential-emotion label table  300  stores a plurality of differential emotions. The differential emotion, which is given by a labeler, represents a label that quantifies an emotion of a speech pattern (second speech pattern) having a speech waveform of a second speech ID relative to a speech pattern (first speech pattern) having a speech waveform of a first speech ID. The first speech ID and the second speech ID indicate speech waveforms of the corresponding speech IDs of the training speech data  111 . 
     It is assumed that the speech waveforms of the first speech ID and the second speech ID have the same speaker ID. The labeler gives an emotion label (of a difference value) between the two inputs. Note that a label of an absolute value of emotion for one speech pattern may be given (held in the training speech data  111 ) as in the related art. In training, the difference between the absolute values may be used as the difference value. For example, when absolute values “0.1” and “0.2” indicating emotions for the speech patterns of the speech IDs “1” and “2”, respectively, are stored in the training speech table  200 , the difference value “0.1” between the absolute value “0.1” of the first speech ID and the absolute value “0.2” of the second speech ID may be calculated as a corresponding differential emotion. 
     Moreover, the training differential-emotion label table  300  may store differential emotions by a plurality of labelers. In this case, a statistical value such as an average value of the differential emotions by the plurality of labelers is used in training. Furthermore, there may be multiple emotion categories instead of one. In this case, the training differential-emotion label table  300  stores a vector value as a differential emotion instead of a scalar value. 
       FIG.  4    is a diagram illustrating an example of a configuration of the differential-emotion recognition apparatus  101 . 
     The differential-emotion recognition apparatus  101  includes a storage device  400 , a CPU  401 , a display  402 , a keyboard  403 , and a mouse  404  as components, like a configuration of a general personal computer (PC). Each component can transmit and receive data via a bus  405 . 
     The storage device  400  includes a training program  411  and a differential-emotion recognition program  421  as programs. These programs are read to the CPU  401  by an operating system (OS) (not illustrated) existing in the storage device  400  at start-up to be executed. 
     The differential-emotion recognition model  113  is, for example, a neural network in which the number of states of an input layer is “1024” in total of the number of features of a first speech pattern “512” and the number of features of a second speech pattern “512”, the number of hidden layers is one and the number of states of the hidden layer is “512”, and the number of states of an output layer is “1”. For the input {xi (i = 1 ... 1024)} of the input layer, the value {h j  (j = 1 ... 512)} of the hidden layer is calculated by (Expression 1). [Mathematical formula 1] 
     
       
         
           
             
               h 
               j 
             
             = 
             s 
             
               
                 
                   
                     ∑ 
                     i 
                   
                   
                     
                       
                         
                           W 
                           
                             i 
                             j 
                           
                           1 
                         
                         
                           x 
                           i 
                         
                         + 
                         
                           b 
                           i 
                           1 
                         
                       
                     
                   
                 
               
             
           
         
       
     
     The output y of the output layer represents a difference value of emotion of the second speech pattern relative to the first speech pattern, and is calculated by (Expression 2). [Mathematical formula 2] 
     
       
         
           
             y 
             = 
             s 
             
               
                 
                   
                     ∑ 
                     i 
                   
                   
                     
                       
                         
                           W 
                           i 
                           2 
                         
                         
                           h 
                           i 
                         
                         + 
                         
                           b 
                           i 
                           2 
                         
                       
                     
                   
                 
               
             
           
         
       
     
     Here, s represents an activation function, for example, a sigmoid function, W represents a weight, and b represents a bias. 
     As a means for obtaining the features from the first speech pattern and the second speech pattern, a statistic or the like for time-series low-level descriptors (LLD), which is described in Document 1 below, can be used. 
     Document 1: Suzuki, “Recognition of Emotions Included in Speech”, the Journal of the Acoustical Society of Japan, Vol. 71, No. 9 (2015), pp. 484-489 
     Note that the present embodiment does not limit the structure of the neural network. Any structure and activation function of a neural network may be used. Furthermore, the differential-emotion recognition model  113  is not limited to a neural network, and may adopt any model. 
     Functions of the differential-emotion recognition apparatus  101  (training program  411 , differential-emotion recognition program  421 , and the like) may be implemented by, for example, the CPU  401  reading a program from the storage device  400  and executing the program (software), may be implemented by hardware such as a dedicated circuit, or may be implemented by a combination of software and hardware. Note that one function of the differential-emotion recognition apparatus  101  may be divided into multiple functions, or multiple functions may be integrated into one function. For example, the differential-emotion recognition program  421  may include an input unit  422 , a processing unit  423 , and an output unit  424 . Moreover, some of the functions of the differential-emotion recognition apparatus  101  may be provided as an alternate function or may be included in another function. Furthermore, some of the functions of the differential-emotion recognition apparatus  101  may be implemented by another computer connectable to the differential-emotion recognition apparatus  101 . For example, the training program  411  may be provided in a first PC, and the differential-emotion recognition program  421  may be provided in a second PC. 
       FIG.  5    is a diagram illustrating an example of a flowchart of the training program  411 . 
     First, the training program  411  assigns initial values to the parameters W ij   1 , b i   1 , W i   2 , and b i   2  of the differential-emotion recognition model  113  (S 501 ). The training program  411  gives random values as the initial values for facilitating the training of the neural network. 
     Next, the training program  411  reads data from the training speech data  111  and the training differential-emotion label data  112  (S 502 ). 
     Next, the training program  411  updates the parameters of the differential-emotion recognition model  113  (S 503 ). As an update method, a neural network back propagation method can be used. 
     Next, the training program  411  determines whether the training is converged (S 504 ). The convergence is determined under conditions that the process has been executed a predetermined fixed number of times, a value of an error function has fallen below a predetermined threshold, and the like. 
       FIG.  6    is a diagram illustrating an example of a flowchart of the differential-emotion recognition program  421 . 
     Before the differential-emotion recognition program  421  is executed, the user  102  or the like stores speech to be analyzed in the storage device  400  as the continuous speech data  121 . 
     First, the differential-emotion recognition program  421  reads one frame of the continuous speech data  121 . If all the continuous speech data  121  has been read (S 601 ), the program ends. 
     Next, the differential-emotion recognition program  421  determines whether a speech section has been detected (S 602 ). A known method can be used to detect a speech section. In the method, for example, when a certain number of frames with a volume level equal to or higher than a given value continue and then a certain number of frames with a volume level equal to or lower than the given value continue, a group of these frames is regarded as a speech section. When no speech sections have been detected, the process returns to S 601 . Note that the speech section may be a section (a group of sequential frames) in which a speech pattern is detected, or may be a section including a previous frame and/or a subsequent frame of the section in which the speech pattern is detected. 
     Next, the differential-emotion recognition program  421  stores information of the detected speech section in the emotion transition data  122  (S 603 ). Note that, in S 603 , a speech section ID and speech section data are stored in the emotion transition data  122  and, in S 606 , an emotion transition is stored. 
     Next, the differential-emotion recognition program  421  determines whether a pair of speech sections can be selected (S 604 ). The pair of speech sections to be selected may be, for example, a pair including temporally adjacent speech sections in the emotion transition data  122 , for one of which no emotion transitions have been calculated. Here, in order to make calculation of an emotion transition robust, the differential-emotion recognition program  421  may use a plurality of pairs of speech sections within a predetermined time interval as the pair of adjacent speech sections. In addition, the differential-emotion recognition program  421  may perform processing of eliminating a pair including a speech section for which emotion recognition is considered to be difficult, such as a short speech section or a speech section with a low volume level. 
     Next, the differential-emotion recognition program  421  inputs a selected pair of speech sections (two pieces of speech data of the speech sections) to the differential-emotion recognition model  113 , and obtains a differential emotion output from the differential-emotion recognition model  113  (S 605 ). 
     Next, the differential-emotion recognition program  421  calculates an emotion transition on the basis of the differential emotion and stores the emotion transition in the emotion transition data  122  (S 606 ). In order to obtain an emotion transition of a certain speech section, for example, the obtained differential emotion may be added to an emotion transition of the other speech section paired with that speech section for all the pairs, and obtained values may be averaged. The initial speech section may have an emotion transition of an average value “0”. Thereafter, the process returns to S 601 . 
     In the configuration illustrated in  FIG.  6   , the differential emotion is acquired by inputting a pair of speech sections detected from the continuous speech data  121  to the differential-emotion recognition model  113 . However, the present invention is not limited to this configuration. For example, the differential-emotion recognition program  421  may be configured to acquire the differential emotion by inputting two pieces of speech data designated by the user to the differential-emotion recognition model  113 . 
       FIG.  7    is a diagram illustrating an example (emotion transition table  700 ) of the emotion transition data  122 . 
     The emotion transition table  700  stores an emotion transition for each speech section. The speech section ID represents a code for uniquely identifying a speech section. The speech section data represents information (for example, time section information) indicating a range of the speech section in the continuous speech data  121 . The emotion transition represents an emotion transition value obtained by the differential-emotion recognition program  421  for the speech section. 
       FIG.  8    is a diagram illustrating an example of a user interface of the differential-emotion recognition apparatus  101 . 
     The user  102  obtains, from the display  402 , information that a continuous speech file to be input can be selected. When the user  102  operates the keyboard  403  and/or the mouse  404  to press a speech file selection button  801  and selects a speech file stored in the differential-emotion recognition apparatus  101 , speech in the speech file is visualized on the display  402  as a waveform  810  and is stored in the storage device  400  as the continuous speech data  121 . The user  102  can subsequently press an analysis start button  802  to run the differential-emotion recognition program  421 . When the emotion transition data  122  is generated, emotion transition values are visualized on the display  402  as a graph  820 . 
     Note that the emotion transition values are calculated for respective speech sections, and thus are smoothly connected in the graph  820  to show the emotion transitions in time series. For that matter, data to be visualized is not limited to the emotion transition values, and may be categories of emotion or a degree (differential-emotion value) for each emotion category. 
     The configuration of the emotion recognition system described above allows for a user to easily look at transition of emotions in speech of a specific speaker using the difference emotion recognition device based on the model that has learned the difference values of emotional expressions in speech. 
     (II) Second Embodiment 
     In the case of the conventional system designed to output absolute emotion assessment values (absolute values of emotion) for input speech, a user might input speech of different persons and use emotion assessment values of the speech for emotional assessments of the persons. In some cases, such usage is inappropriate since an output of an emotion recognition device reflects subjectivity of the minority and is not accurate enough as described above. As a main application, it is often sufficient to show a relative emotion change in speech of the same person by, for example, visualizing rise and fall of emotion expressions. However, a mechanism to limit application thereto is not provided. 
     In this regard, a differential-emotion recognition apparatus  901  according to the present embodiment determines whether two pieces of input speech data come from an identical speaker. In the present embodiment, the same configurations as those of the first embodiment are denoted by the same reference signs, and the description thereof will be omitted. 
       FIG.  9    is a diagram illustrating an example of a processing flow of the differential-emotion recognition apparatus  901  according to the present embodiment. 
     A differential-emotion recognition model  911  according to the present embodiment includes an identical-speaker recognition unit and has, as an output layer, an output z in addition to an output y. The output z, which represents an identical-speaker determination value, is “1” when a first speech pattern and a second speech pattern come from an identical speaker, and “0” when the first speech pattern and the second speech pattern do not come from an identical speaker. The output z is calculated by (Expression 3). [Mathematical formula 3] 
     
       
         
           
             z 
             = 
             s 
             
               
                 
                   
                     ∑ 
                     i 
                   
                   
                     
                       
                         
                           W 
                           i 
                           3 
                         
                         
                           h 
                           i 
                         
                         + 
                         
                           b 
                           i 
                           3 
                         
                       
                     
                   
                 
               
             
           
         
       
     
     Note that the differential-emotion recognition model  911  has parameters W ij   1 , b i   1 , W i   2 , b i   2 , W i   3 , and b i   3 . 
     In the training program  411 , when training is performed using training speech data  111  and training differential-emotion label data  112  similar to those in the first embodiment, a label z is set to “1”. In addition, any two pieces of speech data having different speaker IDs are retrieved from the training speech data  111 . Then, a label y is set to a random value (value unrelated to a differential emotion of the training differential-emotion label data  112 ), and the label z is set to “0” to update the parameters. 
     In the differential-emotion recognition program  421 , when an emotion transition is calculated, speakers are determined not to be identical by an identical-speaker determination value less than a threshold. In a case where a pair of which speakers are determined not to be identical accounts for a certain ratio or more of all the pairs, the emotion transition is an invalid value. When emotion transitions are visualized by the display  402 , an indication that the emotion recognition is invalid is displayed for a result of the speech section of the invalid value. For that matter, the differential-emotion recognition program  421  may stop the analysis in a case where an emotion transition is an invalid value. 
     Note that the present embodiment is not limited to the configuration in which the differential-emotion recognition model  911  includes the identical-speaker recognition unit. For example, the differential-emotion recognition apparatus  901  may include the differential-emotion recognition model  113  and an identical-speaker recognition unit (for example, an identical-speaker recognition model of a neural network). 
     The configuration of the emotion recognition system described above prevents a user from obtaining a result in a case where the user attempts the emotion recognition for speech including speech patterns of different speakers. As a result, it is possible to avoid the usage in which emotion assessment values obtained when speech of different persons is input are regarded as emotional assessments of the persons. 
      (III) Supplementary Notes 
     The above-described embodiments include, for example, the following contents. 
     In the above embodiments, a case where the present invention is applied to an emotion recognition system has been described. However, the present invention is not only applied to such a system but also can be widely applied to other various systems, apparatuses, methods, and programs. 
     In the above embodiments, a process is sometimes described with a “program” as the subject. However, the subject of the process may be a processor unit since the program is executed by the processor unit to perform the defined process appropriately using a storage unit (for example, a memory), an interface unit (for example, a communication port), and/or the like. A process described with a program as the subject may be a process performed by the processor unit or by a device having the processor unit. Furthermore, the processor unit may include a hardware circuit that performs a part or all of the process (for example, a field-programmable gate array (FPGA) or an application specific integrated circuit (ASIC)). 
     In the above embodiments, a part or all of the programs may be installed from a program source into an apparatus such as a computer that implements the differential-emotion recognition apparatus. The program source may be, for example, a program distribution server connected via a network or a computer-readable recording medium (for example, a non-transitory recording medium). Furthermore, in the above description, two or more programs may be implemented as one program, or one program may be implemented as two or more programs. 
     In the above embodiments, the configuration of each table is an example, and one table may be divided into two or more tables, or two or more tables may be combined into one table in whole or in part. 
     In the above embodiments, the screen illustrated and described is an example, and any design may be employed as long as the same information can be received. 
     In the above embodiments, the screen illustrated and described is an example, and any design may be employed as long as the same information can be presented. 
     In the above embodiments, the case where the average value is used as the statistical value has been described. However, the statistical value is not limited to the average value, and may be another statistical value such as a maximum value, a minimum value, a difference between the maximum value and the minimum value, a mode value, a median value, or a standard deviation. 
     In the above embodiments, information is not always output by being displayed on the display. The information may be output as audio output by a speaker, may be output to a file, may be output by being printed on a paper medium or the like by a printing apparatus, may be output by being projected on a screen or the like by a projector, or may be output in another mode. 
     In the above description, information such as programs, tables, and files for implementing the functions may be stored in a storage device such as a memory, a hard disk, and a solid state drive (SSD), or in a recording medium such as an IC card, an SD card, and a DVD. 
     The above-described embodiments have, for example, the following characteristic configurations. 
     (1) An emotion recognition system (for example, the differential-emotion recognition apparatus  101 , the differential-emotion recognition apparatus  901 , a system including the differential-emotion recognition apparatus  101  and a computer capable of communicating with the differential-emotion recognition apparatus  101 , or a system including the differential-emotion recognition apparatus  901  and a computer capable of communicating with the differential-emotion recognition apparatus  901 ) includes: an input unit (for example, the differential-emotion recognition program  421 , the input unit  422 , or a circuit) configured to input first speech data and second speech data; and a processing unit (for example, the differential-emotion recognition program  421 , the processing unit  423 , or a circuit) configured to input the first speech data and the second speech data to a differential-emotion recognition model (for example, the differential-emotion recognition model  113  or the differential-emotion recognition model  911 ) that infers a differential emotion between two pieces of speech data, and acquire, from the differential-emotion recognition model, differential-emotion information indicating a differential emotion between the first speech data and the second speech data. Note that the first speech data and the second speech data may be included in one speech data source (for example, the continuous speech data  121 ) or may be derived from separate speech data sources. 
     In the above configuration, for example, since the differential emotion (that is, relative emotion) is inferred from the two pieces of speech data, an emotion recognized by the emotion recognition system can be closer to an actual user impression than an emotion inferred from one piece of speech data (that is, absolute emotion). 
     (2) The emotion recognition system further includes: a determination unit (for example, the differential-emotion recognition program  421  including the determination unit, the determination unit, or a circuit) configured to input the first speech data and the second speech data to an identical-speaker recognition unit (for example, the differential-emotion recognition model  911  or the identical-speaker recognition unit) that infers whether speakers of two pieces of speech data are identical, acquire, from the identical-speaker recognition unit, determination information indicating that a speaker of the first speech data and a speaker of the second speech data are identical (for example, “0” for indicating they are not identical or “1” for indicating they are identical, or a value from “0” to “1”), and determine whether the speaker of the first speech data and the speaker of the second speech data are identical according to the acquired determination information; and an output unit (for example, the differential-emotion recognition program  421  including the determination unit, the output unit  424 , or a circuit) configured to output information according to a result of determination by the determination unit. 
     According to the above configuration, it is determined whether the speakers of the two pieces of speech data are identical. This allows for avoiding incorrect usage in which, for example, speech of different persons is input for emotional assessments of the persons. 
     (3) The output unit is configured to, when the determination unit determines that the speaker of the first speech data and the speaker of the second speech data are identical, output the differential-emotion information (for example, the graph  820 ) acquired by the processing unit, and the output unit is configured to, when the determination unit determines that the speaker of the first speech data and the speaker of the second speech data are not identical, output information that the first speech data and the second speech data do not come from an identical speaker (for example, “A speech section of which speakers are not identical is included. This speech section is hidden in the graph.”). 
     According to the above configuration, for example, a mechanism to reject comparison between speech patterns of different persons can be provided. 
     (4) The input unit is configured to input continuous speech data (for example, the continuous speech data  121 ), and the processing unit is configured to detect at least one speech section from the continuous speech data input by the input unit, extract a piece of speech data of each of the at least one speech section from the continuous speech data, select, out of extracted pieces of speech data, a first piece of speech data and a second piece of speech data within a predetermined time interval from the first piece of speech data, input the first piece of speech data and the second piece of speech data to the differential-emotion recognition model, and acquire differential-emotion information indicating a differential emotion between the first piece of speech data and the second piece of speech data (for example, see  FIG.  6   ). 
      According to the above configuration, when the continuous speech data is input, for example, the differential-emotion information indicating the differential emotion between the two adjacent pieces of speech data is sequentially acquired, so that the user can grasp transition of emotions. 
     (5) The differential-emotion recognition model is trained by using two pieces of speech data of a same person and differential-emotion information indicating a differential emotion between the two pieces of speech data (see  FIG.  5   ). 
     In the above configuration, labeling is performed on the two pieces of speech data of the same person. Therefore, for example, a labeler easily estimates emotions, and likelihood of labels depending on subjectivity of the labeler can be reduced. 
     (6) When the identical-speaker recognition unit is trained by using two pieces of speech data, differential-emotion information indicating a differential emotion between the two pieces of speech data, and information indicating whether speakers of the two pieces of speech data are identical, in a case where the two pieces of speech data come from different persons, training is performed by changing the differential-emotion information indicating the differential emotion between the two pieces of speech data to a value (for example, a random value) unrelated to the differential-emotion information. 
     According to the above configuration, for example, the differential-emotion recognition model and the identical-speaker recognition unit can be trained by using common data, so that a burden of preparing data used for training can be reduced. 
     (7) The differential-emotion recognition model is a neural network. 
     In the above configuration, since the differential-emotion recognition model is a neural network, it is possible to reduce likelihood of a rule functioning incorrectly due to the influence of variable factors of speaker characteristics such as a voice tendency to be muffled and a high voice, environmental characteristics such as large reverberation, and the like. It is also possible to improve the inference accuracy. 
     In addition, the above configurations may be appropriately changed, rearranged, combined, or omitted without departing from the gist of the present invention. 
     It should be understood that items included in a list in the format “at least one of A, B, and C” can mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Similarly, items listed in the format “at least one of A, B, or C;” can mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C) .