Patent Publication Number: US-2023134696-A1

Title: Spoken sentence processing apparatus, method, and non-transitory computer readable medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2021-178503, filed Nov. 1, 2021, the entire contents of which are incorporated herein by reference. 
     FIELD 
     Embodiments described herein relate generally to a spoken sentence processing apparatus, method, and non-transitory computer readable medium. 
     BACKGROUND 
     A spoken dialog system that performs a motion desired by a user by repeating an interaction with the user a plurality of times is expected to be utilized in many scenes. In order to smoothly progress the interaction and avoid a malfunction, the spoken dialog system should correctly identify a speech from the user into a supportable speech belonging to a domain targeted by the system (hereinafter, also referred to as a “valid speech”) and an unsupportable speech not belonging to the domain (hereinafter, also referred to as an “invalid speech”). In particular, it is desirable that the spoken dialog system does not perform a predetermined operation with respect to the invalid speech, and takes a measure such as asking back the user an intention or an ambiguous point of the speech or notifying that it cannot cope with the user. Therefore, the spoken dialog system is required to recognize the invalid speech with high accuracy. 
     In order to achieve the above object, firstly there is a method of training a spoken dialog system using speech examples regarding a large number of invalid speeches collected in advance by an engineer (for example, AI engineer). However, it is practically difficult to collect speech examples, and it is difficult to achieve the object with a general rule or a method based on machine learning. Second, there is a method in which an engineer collects an example of an invalid speech from a log of the spoken dialog system and trains the spoken dialog system using the collected example of the invalid speech. However, the method is not realistic in view of the time and physical cost required for the engineers. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram showing a functional configuration example of a spoken sentence processing apparatus according to a first embodiment. 
         FIG.  2    is a flowchart showing an operation example of the spoken sentence processing apparatus according to the first embodiment. 
         FIG.  3    is a block diagram showing a functional configuration example of a spoken sentence processing apparatus according to a second embodiment. 
         FIG.  4    is a flowchart showing an operation example of the spoken sentence processing apparatus according to the second embodiment. 
         FIG.  5    is a diagram showing an example of a dialogue realized by the spoken sentence processing apparatus according to the second embodiment. 
         FIGS.  6 A and  6 B  are diagrams showing an example of a dialogue realized by the spoken sentence processing apparatus according to the second embodiment. 
         FIGS.  7 A and  7 B  are diagrams showing an example of a dialogue realized by the spoken sentence processing apparatus according to the second embodiment. 
         FIG.  8    is a block diagram showing a functional configuration example of a spoken sentence processing apparatus according to a third embodiment. 
         FIG.  9    is a flowchart showing an operation example of the spoken sentence processing apparatus according to the third embodiment. 
         FIG.  10    is a block diagram showing a hardware configuration example of the spoken sentence processing apparatus according to the first to third embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     In general, according to one embodiment, a spoken sentence processing apparatus includes a processing circuit. The processing circuit receives a spoken sentence from a user. The processing circuit estimates an intention expressed by the user based on the spoken sentence. The processing circuit estimates a search condition specified by the user based on the spoken sentence. The processing circuit determines whether the spoken sentence is an invalid speech respectively in a first estimation result related to the estimated intention and a second estimation result related to the estimated search condition. 
     Hereinafter, a spoken sentence processing apparatus, a method, and a non-transitory computer readable medium according to an embodiment will be described with reference to the drawings. In the following embodiments, parts denoted by the same reference numerals perform similar operations, and redundant description will be omitted as appropriate. 
     First Embodiment 
       FIG.  1    is a block diagram showing a functional configuration example of a spoken sentence processing apparatus  1  according to a first embodiment. The spoken sentence processing apparatus  1  is an apparatus that performs various operations by processing a spoken sentence from a user, and is an example of a spoken dialog system. For example, the spoken sentence processing apparatus  1  is a spoken dialog system for searching that performs information search regarding a restaurant using a spoken sentence from a user. In a case where a spoken sentence (for example: “Find a Japanese restaurant.”, “Is there any Japanese restaurant?”, “I want to eat Japanese food.”, and “I prefer Japanese food.”) intended for information search regarding a restaurant is input, the spoken sentence processing apparatus  1  searches for a restaurant that serves Japanese food from a plurality of restaurants, and notifies the user of a search result such as a restaurant name and location information. On the other hand, in a case where a spoken sentence (for example: “Speaking of which, a Japanese restaurant has opened in the neighborhood”, “a shop that sells Japanese clothing”) not intended for the information search is input, the spoken sentence processing apparatus  1  notifies the user that, for example, the spoken sentence processing apparatus  1  cannot cope with the spoken sentence without performing the information search for restaurants. In order to realize such an operation, the spoken sentence processing apparatus  1  according to the first embodiment includes a reception unit  111 , a first estimation unit  112 , a second estimation unit  113 , and a determination unit  114 . 
     The reception unit  111  receives a spoken sentence from a user. The spoken sentence may be a text input by the user via an input unit such as a keyboard or a text converted from the user&#39;s voice by voice recognition. Subsequently, the reception unit  111  outputs the received spoken sentence to each of the first estimation unit  112  and the second estimation unit  113 . 
     The first estimation unit  112  estimates an intention expressed by the user based on the spoken sentence. Specifically, the first estimation unit  112  estimates the intention of the spoken sentence by applying an intention estimation model  121  to the spoken sentence received from the reception unit  111 . Then, the first estimation unit  112  outputs an estimation result by the intention estimation model  121  (an intention estimation result; a first estimation result) to the determination unit  114 . 
     The intention estimation model  121  estimates a probability (first probability) that a spoken sentence is relevant to a class (intention class) corresponding to each of a plurality of candidates regarding an intention as the estimation result (first estimation result). Specifically, the intention estimation model  121  is a classification model based on deep learning, and outputs a probability that an input spoken sentence is relevant to each of a plurality of preset intention classes. Parameters of the intention estimation model  121  may be trained in advance using training data. Note that the intention estimation model  121  may adopt an existing intention estimation method (for example, mood analysis) based on the entire spoken sentence. 
     First, if a spoken sentence is, for example, “Is there any Japanese restaurant?”, “I want to eat Japanese food.”, or “I prefer Japanese food.”, the intention of the spoken sentence is “specifying search conditions”. Second, if a spoken sentence is, for example, “Hello” or “Thank you”, the intention of the spoken sentence is “greeting”. Third, if the spoken sentence is, for example, “Yes” or “No”, the intention of the spoken sentence is “agreeing or disagreeing”. Fourth, if the spoken sentence is, for example, “Start over.” or “Show me the previous restaurant again.”, the intention of the spoken sentence is “operating the system”. As described above, since different intentions are included for each spoken sentence from the user, the intention estimation model  121  only needs to have an intention class corresponding to each intention of a spoken sentence assumed in advance. 
     When an engineer trains the intention estimation model  121 , for example, an intention class is set for each of K (K is a natural number) intentions previously assumed such as an intention class 1 for “specifying search conditions”, an intention class 2 for “greeting”, an intention class 3 for “agreeing or disagreeing”, an intention class 4 for “operating the system”, and the like. At this time, the engineer regards “invalid speech” as one intention similarly to other intentions, and sets an intention class K+1 to “invalid speech”. Subsequently, the engineer assigns any one of the intention classes 1 to K+1 to each of the examples of the plurality of spoken sentences collected in advance, and creates labeled training data. The engineer may then train the parameters of the intention estimation model  121  using the labeled training data. As a result, the intention estimation model  121  is trained to classify an example of a plurality of spoken sentences collected in advance into any one of the intention classes 1 to K+1. The trained intention estimation model  121  outputs not only a probability that a spoken sentence from the user is relevant to each of the intention classes 1 to K but also a probability that the spoken sentence is relevant to the intention class K+1. 
     In the above example, the engineer may not set the intention class K+1 as “invalid speech”. In this case, the engineer assigns any one of the intention classes 1 to K to each of the examples of the plurality of spoken sentences collected in advance, and creates labeled training data. The engineer may then train the parameters of the intention estimation model  121  using the labeled training data. As a result, the intention estimation model  121  is trained to classify an example of a plurality of spoken sentences collected in advance into any one of the intention classes 1 to K. The trained intention estimation model  121  outputs a probability that a spoken sentence from the user is relevant to each of the intention classes 1 to K. 
     The second estimation unit  113  estimates a search condition specified by the user based on the spoken sentence. Specifically, the second estimation unit  113  estimates the search condition of the spoken sentence by applying at least one search condition estimation model  122  to the spoken sentence received from the reception unit  111 . Subsequently, the second estimation unit  113  outputs an estimation result (a search condition estimation result; a second estimation result) by the search condition estimation model  122  to the determination unit  114 . At this time, the same number of search condition estimation models  122  as the number of attributes used as search conditions may be applied. 
     The search condition estimation model  122  estimates a probability (second probability) that the spoken sentence is relevant to each of the plurality of values related to the search condition as the estimation result (second estimation result). Specifically, the search condition estimation model  122  is a model based on deep learning, and outputs a probability that an input spoken sentence is relevant to each of a plurality of preset values. Note that the search condition estimation model  122  may output a probability relevant to each value by focusing on a keyword included in the spoken sentence. 
     According to the present embodiment, it is assumed that a plurality of values corresponding to one attribute is set in advance in one search condition estimation model  122 . For example, a plurality of values (for example, Japanese food, western food, Chinese food, Asian/Ethnic food) corresponding to the food genre are set in the search condition estimation model  122  for the food genre. On the other hand, a plurality of values (for example, around A station, B shopping street, suburb of C city, within a walking distance of X minutes, within a direct distance of Y meters) corresponding to the place is set in the search condition estimation model  122  for a place. Meanwhile, a plurality of values (for example, relatively high, normal, relatively cheap) corresponding to the price range is set in the search condition estimation model  122  for the price range. Each value may be a value extracted from a list of a plurality of restaurants to be searched. Furthermore, each value is not limited to a specific value that designates a predetermined value, and may include an empty value that does not designate a predetermined value. In other words, an empty value is a value that the search condition estimation model  122  does not assume as the search target. 
     The determination unit  114  determines whether or not the spoken sentence is an invalid speech in each of the estimation result regarding the estimated intention (first estimation result) and the estimation result regarding the estimated search condition (second estimation result). Specifically, the determination unit  114  receives the first estimation result from the first estimation unit  112  and the second estimation result from the second estimation unit  113 , and determines whether the spoken sentence is an invalid speech in each estimation result. 
     According to the present embodiment, the determination unit  114  calculates a plurality of types of determination results. First, the determination unit  114  calculates a determination result (first determination result) indicating that the spoken sentence has been determined not to be an invalid speech in each of the first estimation result and the second estimation result. Secondly, the determination unit  114  calculates a determination result (second determination result) indicating that the spoken sentence has been determined to be an invalid speech in either the first estimation result or the second estimation result. Thirdly, the determination unit  114  calculates a determination result (third determination result) indicating that the spoken sentence has been determined to be an invalid speech in each of the first estimation result and the second estimation result. Among these, the second determination result includes a determination result (fourth determination result) indicating that the spoken sentence has been determined to be an invalid speech in the first estimation result and that the spoken sentence has not been determined to be an invalid speech in the second estimation result. Furthermore, the second determination result includes a determination result (fifth determination result) indicating that the spoken sentence has been determined not to be an invalid speech in the first determination result and that the spoken sentence has been determined to be an invalid speech in the second estimation result. 
     In the first estimation result, the determination unit  114  determines whether or not the user&#39;s spoken sentence is an invalid speech. For example, the determination unit  114  specifies an intention class to which the highest probability is given among the probabilities relevant to the intention classes 1 to K+1 output from the first estimation unit  112 . In a case where the specified intention class is the intention class K+1 (which is the intention class corresponding to the invalid speech), the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. Conversely, in a case where the specified intention class is not the intention class K+1, the determination unit  114  determines that the user&#39;s spoken sentence is not an invalid speech (which is valid speech). 
     For example, it is assumed that a probability that a spoken sentence is relevant to the intention class 1 “specifying search conditions” is 0.1, probabilities that is relevant to the intention classes 2 to K are all 0, and a probability that is relevant to the intention class K+1 “invalid speech” is 0.9. In this case, since the intention class to which the highest probability is given is the intention class K+1, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. 
     Alternatively, in a case where each probability relevant to each of the intention classes 1 to K output from the first estimation unit  112  is equal to or less than a predetermined threshold value, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. The predetermined threshold value can be set to an arbitrary value by an engineer who manages the spoken sentence processing apparatus  1 . Conversely, in a case where at least one of the probabilities relevant to the intention classes 1 to K exceeds a predetermined threshold value, it is determined that the user&#39;s spoken sentence is not an invalid speech (which is a valid speech). Note that the determination unit  114  may determine that the spoken sentence is an invalid speech in a case where all probabilities are less than a predetermined threshold value, and may determine that the spoken sentence is not an invalid speech in a case where at least one probability is equal to or greater than a predetermined threshold value. 
     For example, it is assumed that the probabilities relevant to the intention classes 1 to K are all less than 0.5 and the predetermined threshold value is 0.5. In this case, since the probabilities relevant to the intention classes 1 to K are all equal to or less than the predetermined threshold value, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. 
     As described above, the determination unit  114  may determine whether or not the user&#39;s spoken sentence is an invalid speech in the first estimation result based on the probability that the spoken sentence is relevant to the intention class K+1 or based on the probability that the spoken sentence is relevant to the intention classes 1 to K. Both approaches may be applied in combination with each other, or only one of them may be applied. 
     On the other hand, the determination unit  114  determines whether or not the user&#39;s spoken sentence is an invalid speech in the second estimation result. For example, in a case where the probabilities relevant to the plurality of values related to the search condition output from the second estimation unit  113  are equal to or less than a predetermined threshold value, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. The predetermined threshold value can be set to an arbitrary value by an engineer who manages the spoken sentence processing apparatus  1 . Conversely, in a case where at least one of the probabilities relevant to the plurality of values related to the search condition exceeds the predetermined threshold value, it is determined that the user&#39;s spoken sentence is not an invalid speech (which is a valid speech). Note that the determination unit  114  may determine that the spoken sentence is an invalid speech in a case where all probabilities are less than a predetermined threshold value, and may determine that the spoken sentence is not an invalid speech in a case where at least one probability is equal to or greater than a predetermined threshold value. 
     For example, the determination unit  114  focuses on the probability relevant to each of the plurality of values regarding the food genre output from the search condition estimation model  122  for the food genre. Here, it is assumed that the search condition estimation model  122  for the food genre outputs the probability that the search condition of the spoken sentence is relevant to each of four types of values (Japanese food, western food, Chinese food, Asian/Ethnic food). For example, it is assumed that the probabilities relevant to “Japanese food” to “Asian/Ethnic food” are all less than 0.5, and the predetermined threshold value is 0.5. In this case, since the probabilities relevant to the respective values are all equal to or less than the predetermined threshold value, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. 
     Subsequently, the determination unit  114  performs determination similar to the above for the search condition estimation models  122  other than the search condition estimation model  122  for a food genre. Specifically, the determination unit  114  determines whether or not the user&#39;s spoken sentence is an invalid speech for each of the search condition estimation models  122  including the search condition estimation model  122  for a place and the search condition estimation model  122  for a price range. When it is determined in each search condition estimation model  122  that the spoken sentence is an invalid speech, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. Conversely, when the at least one search condition estimation model  122  determines that the spoken sentence is not an invalid speech, the determination unit  114  determines that the user&#39;s spoken sentence is not an invalid speech. 
     Alternatively, the determination unit  114  specifies a value to which the highest probability is assigned among the probabilities relevant to the plurality of values related to the search condition. In a case where the specified value is an empty value (that is, a value for which a specific value is not specified), the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. Operations similar to those described above may be performed for each of the plurality of search condition estimation models  122 . In a case where an empty value is specified in all the attributes targeted by the plurality of search condition estimation models  122 , the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech. Conversely, in a case where an empty value is not specified in the at least one search condition estimation model  122 , the determination unit  114  determines that the user&#39;s spoken sentence is not an invalid speech. 
       FIG.  2    is a flowchart showing an operation example of the spoken sentence processing apparatus  1  according to the first embodiment. The start point in the present operation example can be a point in time when the spoken sentence processing apparatus  1  receives a spoken sentence from the user for the first time, or a point in time when the spoken sentence processing apparatus  1  receives a next spoken sentence after receiving a plurality of spoken sentences from the user. 
     In step S 101 , the reception unit  111  receives a spoken sentence from the user, and outputs the received spoken sentence to each of the first estimation unit  112  and the second estimation unit  113 . 
     In step S 102 , the first estimation unit  112  estimates the user&#39;s speech intention by applying the intention estimation model  121  to the spoken sentence received from the reception unit  111 . Subsequently, the first estimation unit  112  outputs the estimation result (first estimation result) to the determination unit  114 . 
     In step S 103 , the second estimation unit  113  applies at least one search condition estimation model  122  to the spoken sentence received from the reception unit  111  to estimate the search condition of the user. Subsequently, the second estimation unit  113  outputs the estimation result (second estimation result) to the determination unit  114 . 
     In step S 104 , the determination unit  114  receives the estimation result of the intention from the first estimation unit  112  and the estimation result of the search condition from the second estimation unit  113 , and determines whether or not the user&#39;s spoken sentence is an invalid speech in each of the received estimation results. Subsequently, the determination unit  114  calculates a plurality of types of determination results (first determination result, second determination result, third determination result). 
     Note that steps S 102  and S 103  may be executed simultaneously, or step S 103  may be executed prior to step S 102 . Furthermore, following step S 104 , the spoken sentence processing apparatus  1  may wait for reception of the next spoken sentence after ending the series of processing. 
     The spoken sentence processing apparatus  1  according to the first embodiment has been described above. The spoken sentence processing apparatus  1  according to the first embodiment determines whether or not a spoken sentence from a user is an invalid speech using each of an estimator based on intention estimation and an estimator based on search condition estimation. Here, a case where it is determined that the spoken sentence is not an invalid speech in one estimation result and it is determined that the spoken sentence is an invalid speech in the other estimation result (that is, in a case where the two types of estimation results contradict each other) is assumed. As described above, even in a case where one estimator has not been able to detect an invalid speech, the spoken sentence processing apparatus  1  can recognize the invalid speech using the estimation result from the other estimator. Therefore, the spoken sentence processing apparatus  1  can recognize an invalid speech with higher accuracy than a case where only one type of estimator is used. 
     Furthermore, in the above case, the spoken sentence processing apparatus  1  according to the first embodiment can obtain information regarding which one of the estimator based on the intention estimation and the estimator based on the search condition estimation has not been able to detect the invalid speech. Since the spoken sentence processing apparatus  1  according to the second embodiment generates the response sentence based on the information, it is possible to advance more flexible interaction as compared with a case where only one type of estimator is used. 
     Second Embodiment 
       FIG.  3    is a block diagram showing a functional configuration example of a spoken sentence processing apparatus  1  according to a second embodiment. The spoken sentence processing apparatus  1  according to the second embodiment further includes a response generation unit  115  and an output unit  116  in addition to the components (reception unit  111 , first estimation unit  112 , second estimation unit  113 , and determination unit  114 ) included in the spoken sentence processing apparatus  1  according to the first embodiment. 
     The response generation unit  115  generates response sentences respectively corresponding to a first determination result, a second determination result, and a third determination result. As described above, the first determination result is a determination result indicating that a spoken sentence has been determined to be a valid speech by both the estimators, the second determination result is a determination result indicating that the spoken sentence has been determined to be a valid speech by one of the estimators and has been determined to be an invalid speech by the other estimator, and the third determination result is a determination result indicating that the spoken sentence has been determined to be an invalid speech by both the estimators. Furthermore, the fourth determination result included in the second determination result is a determination result indicating that the spoken sentence has been determined to be an invalid speech by the intention estimator and determined to be a valid speech by the search condition estimation unit. On the other hand, the fifth determination result included in the second determination result is a determination result indicating that the spoken sentence has been determined to be a valid speech by the intention estimator and determined to be an invalid speech by the search condition estimation unit. 
     Specifically, the response generation unit  115  generates a response sentence with reference to a response sentence template  123  corresponding to each determination result output from the determination unit  114 . Subsequently, the response generation unit  115  outputs the generated response sentence to the output unit  116 . Note that the response generation unit  115  may generate a response sentence by further referring to the estimation result (first estimation result) from the first estimation unit  112  and the estimation result (second estimation result) from the second estimation unit  113 . 
     The response sentence template  123  is a plurality of fixed phrases prepared in advance for each determination result. First, as the response sentence template  123  corresponding to the first determination result, for example, a fixed phrase (for example, “How about an XX restaurant?”) including a placeholder (XX) in which a predetermined search result can be embedded is prepared. The predetermined search result includes a search result related to each of a food genre, a place, and a price range. 
     Secondly, as the response sentence template  123  corresponding to the fourth determination result among the second determination results, a fixed phrase (for example, “Is XX acceptable for food category?”) including a placeholder (XX) in which a predetermined search result can be embedded and prompting the user to confirm the search condition estimated by the second estimation unit  113  is prepared. 
     On the other hand, as the response sentence template  123  corresponding to the fifth determination result in the second determination result, a fixed phrase corresponding to the intention estimated by first estimation unit  112  is prepared. For example, if the estimated intention is “specifying search conditions”, a fixed phrase (for example, “What kind of food would you like?”) that prompts the user to specify at least one search condition is prepared. This is because it is assumed that the user has specified the attribute of the search condition not supported by the spoken sentence processing apparatus  1 . Alternatively, a fixed phrase (for example, “search cannot be performed under the condition”) indicating that the search cannot be performed under the search condition input by the user may be prepared. This is because it is assumed that the user specifies an unknown value relevant to the attribute of the search condition handled by the spoken sentence processing apparatus  1 . 
     Thirdly, as the response sentence template  123  corresponding to the third determination result, a fixed phrase (for example, “Is there any other information you want to know?”) notifying that the spoken sentence processing apparatus  1  cannot cope with is prepared. 
     The output unit  116  outputs the response sentence. Specifically, the output unit  116  receives the response sentence from the response generation unit  115  and outputs the received response sentence to the user. The output unit  116  may output, for example, a response sentence to a display device (for example, a display, a smartphone), and the display device may display the response sentence to the user. Alternatively, the output unit  116  may output the response sentence to an audio device (for example, a speaker, a smartphone), and the audio device may convert the response sentence into a voice and reproduce the voice for the user. According to any one of the output modes, the user can visually or aurally recognize the response sentence for the spoken sentence. 
       FIG.  4    is a flowchart showing an operation example of the spoken sentence processing apparatus  1  according to the second embodiment. Steps S 201  to S 204  according to the second embodiment are similar to steps S 101  to S 104  according to the first embodiment. Note that, in step S 204 , it is assumed that the determination unit  114  has calculated any one of the first determination result, the second determination result, and the third determination result described above for the spoken sentence from the user. 
     In step S 205 , the response generation unit  115  branches the process according to the type of the determination result received from the determination unit  114 . Firstly, when receiving the first determination result (that is, in a case where it is determined that the spoken sentence is a valid speech in both estimation results), the response generation unit  115  proceeds to step S 206 . Secondly, when receiving the second determination result (that is, in a case where it is determined that the spoken sentence is an invalid speech only in one estimation result), the response generation unit  115  proceeds to step S 207 . Thirdly, when receiving the third determination result (that is, in a case where it is determined that the spoken sentence is an invalid speech in both estimation results), the response generation unit  115  proceeds to step S 208 . 
     In step S 206 , the response generation unit  115  generates a response sentence for presenting the search result based on the estimation result of the intention, the estimation result of the search condition, and the response sentence template  123 . For example, the response generation unit  115  searches for information relevant to a specific condition based on the estimated search condition and calculates a search result. Subsequently, the response generation unit  115  generates a response sentence by embedding the calculated search result in the response sentence template  123  corresponding to the search result. 
     In step S 207 , the response generation unit  115  generates a response sentence for prompting the user to confirm based on the estimation result of the intention, the estimation result of the search condition, and the response sentence template  123 . For example, the response generation unit  115  generates a response sentence by selecting the response sentence template  123  for explaining the situation among the plurality of response sentence templates  123 . 
     In step S 208 , the response generation unit  115  generates a response sentence indicating that the user&#39;s spoken sentence is an invalid speech based on the intention estimation result, the search condition estimation result, and the response sentence template  123 . For example, the response generation unit  115  generates a response sentence by selecting the response sentence template  123  indicating that the response sentence cannot be made. 
     In step S 209 , the output unit  116  receives the response sentence from the response generation unit  115 , and outputs the received response sentence to an output apparatus such as a display device or an audio device. The output apparatus outputs the response sentence to the user. 
     Note that, following step S 209 , the spoken sentence processing apparatus  1  may end a series of processes and wait for reception of the next spoken sentence. 
       FIGS.  5 ,  6 A and  6 B, and  7 A and  7 B  are diagrams showing an example of a dialogue realized by the spoken sentence processing apparatus  1  according to the second embodiment.  FIG.  5    illustrates three spoken sentences from the user and a response sentence of the spoken sentence processing apparatus  1  for each spoken sentence. The spoken sentence processing apparatus  1  receives spoken sentences U 1 , U 2 , and U 3  from the user, estimates intentions I 1 , I 2 , and I 3  of the spoken sentences and search conditions C 1 , C 2 , and C 3 , and then outputs response sentences S 1 , S 2 , and S 3 . Each intention is estimated by the intention estimation model  121 . Each search condition includes a combination of three attributes (food genre, place, price range), and each value is estimated by the search condition estimation model  122  corresponding to each attribute. 
     For example, in the first turn (U 1 -S 1 ) of the interaction, based on a spoken sentence U 1  “Find a Japanese restaurant on the north side of the station.” from the user, the spoken sentence processing apparatus  1  estimates an intention I 1  “specifying search conditions” and a search condition C 1  “food genre: Japanese food, place: north side of station, Price range:” for the spoken sentence U 1 . Here, the price range that is one attribute related to the search condition C 1  is an empty value. Therefore, based on the estimated intention I 1  and the search condition C 1 , the spoken sentence processing apparatus  1  outputs a response sentence S 1  “Do you have a preference for the price range?” that asks the user about the search condition regarding the price range. As described above, in a general situation, when the estimated intention is “specifying search conditions”, a specific value is estimated with any one attribute related to the search condition. 
     On the other hand, in the third turn of the interaction (U 3 -S 3 ), based on a spoken sentence U 3  “Sounds good. It is close from here.” from the user, the spoken sentence processing apparatus  1  estimates an intention  13  “invalid speech” and a search condition C 3  “food genre: place: price range:” for the spoken sentence U 3 . Here, each attribute related to the search condition C 3  is an empty value. Therefore, the spoken sentence processing apparatus  1  outputs a response sentence S 3  “Is there any other information you want to know?” indicating that the response sentence cannot be made. 
     In  FIG.  6 A , the spoken sentence processing apparatus  1  estimates an intention Ill “invalid speech” and a search condition C 11  “food genre: Japanese food, place: price range:” for a spoken sentence U 11  based on a spoken sentence U 11  “I like Japanese food.” from the user. Subsequently, the spoken sentence processing apparatus  1  determines that the spoken sentence U 11  is an invalid speech in the result of the intention estimation, and determines that the spoken sentence U 11  is a valid speech in the result of the search condition estimation (corresponding to the fourth determination result). Therefore, the spoken sentence processing apparatus  1  outputs a response sentence S 11  “Do you prefer Japanese food for food genres?” that prompts the user to confirm “Japanese food”, which is the estimated search condition. 
     In  FIG.  6 B , a case is assumed where the spoken sentence processing apparatus according to a conventional example includes only an estimator related to intention estimation but does not include an estimator related to search condition estimation. The apparatus estimates an intention  112  “invalid speech” for a spoken sentence U 11  based on a spoken sentence U 12  similar to the spoken sentence U 12 . Therefore, the apparatus outputs a response sentence S 12  “Is there any other information you want to know?” notifying the user that the response sentence cannot be made. In other words, even in the same situation, the apparatus including only one type of estimator cannot proceed with flexible interaction such as generating a response sentence that prompts the user to confirm as in the spoken sentence processing apparatus  1  including two types of estimators. 
     In  FIG.  7 A , based on a spoken sentence U 21  “I want to eat something.” from the user, the spoken sentence processing apparatus  1  estimates an intention  121  “specifying search conditions” and a search condition C 21  “food genre: place: price range:” for the spoken sentence U 21 . Subsequently, the spoken sentence processing apparatus  1  determines that the spoken sentence U 21  is a valid speech in the intention estimation result, and determines that the spoken sentence U 21  is an invalid speech in the search condition estimation result (corresponding to the fifth determination result). Therefore, the spoken sentence processing apparatus  1  outputs a response sentence S 21  “Which food genre would you like?” that prompts the user to specify at least one search condition. 
     In  FIG.  7 B , it is assumed that the spoken sentence processing apparatus according to a conventional example includes only an estimator related to search condition estimation and does not include an estimator related to intention estimation. Based on a spoken sentence U 22  similar to the spoken sentence U 21 , the apparatus estimates a search condition C 22  “food genre: place: price range:” for the spoken sentence U 22 . Therefore, the apparatus outputs a response sentence S 22  “Is there any other information you want to know?” notifying the user that the response sentence cannot be made. In other words, even in the same situation, the apparatus including only one kind of estimator cannot perform a flexible interaction such as generating a response sentence that prompts the user to specify at least one search condition as in the spoken sentence processing apparatus  1  including two kinds of estimators. 
     The spoken sentence processing apparatus  1  according to the second embodiment has been described above. The spoken sentence processing apparatus  1  according to the second embodiment determines whether or not a spoken sentence from a user is an invalid speech using each of an estimator based on intention estimation and an estimator based on search condition estimation. Subsequently, the spoken sentence processing apparatus  1  generates and outputs an appropriate response sentence based on each estimation result obtained from each estimator. 
     For example, in a case where a spoken sentence has been estimated to be an invalid speech in the intention estimation, while “food genre: Japanese food” has been estimated as a search condition for the spoken sentence in the search condition estimation, the spoken sentence processing apparatus  1  can output a response sentence indicating to inquire the user about the search condition. In a case where the user agreed the response sentence, the spoken sentence processing apparatus  1  can recognize that the intention estimation has failed. Conversely, in a case where the user denies the response sentence, the spoken sentence processing apparatus  1  can recognize that the search condition estimation has failed. In other words, the spoken sentence processing apparatus  1  can classify the cause regarding which estimator has failed in the estimation. Furthermore, the spoken sentence processing apparatus  1  can return the response sentence corresponding to each cause by using the fixed phrase of the response sentence corresponding to the failure cause. Therefore, the spoken sentence processing apparatus  1  can advance a more flexible interaction. 
     Third Embodiment 
       FIG.  8    is a block diagram showing a functional configuration example of a spoken sentence processing apparatus  1  according to a third embodiment. The spoken sentence processing apparatus  1  according to the third embodiment further includes a storage unit  117  and a sample memory unit  124  in addition to the configurations (the reception unit  111 , first estimation unit  112 , second estimation unit  113 , and determination unit  114 ) included in the spoken sentence processing apparatus  1  according to the first embodiment. 
     As a result of an intention estimation (first estimation result), the first estimation unit  112  estimates a similarity between a feature amount of a spoken sentence from a user and a feature amount of a spoken sentence in each of a plurality of samples stored in the sample memory unit  124 . Specifically, the first estimation unit  112  extracts the feature amount of the spoken sentence by applying the intention estimation model  121  to the spoken sentence received from the reception unit  111 . Subsequently, the first estimation unit  112  estimates similarity between the extracted feature amount and the feature amount of the spoken sentence in each of the plurality of samples stored in the sample memory unit  124 . Thereafter, the first estimation unit  112  outputs the estimated similarity to the determination unit  114 . Note that the first estimation unit  112  may output the feature amount of the spoken sentence from the user to the storage unit  117 . 
     The similarity is, for example, cosine similarity between vectors representing feature amounts of spoken sentences. The vector is, for example, a vector in a hidden state calculated by a model based on deep learning such as the intention estimation model  121 . In a case where each cosine similarity is equal to or less than a predetermined threshold value, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech in the first estimation result. Alternatively, the similarity may be a Euclidean distance between the vectors. In this case, when each Euclidean distance is larger than the predetermined threshold value, the determination unit  114  determines that the user&#39;s spoken sentence is an invalid speech in the first estimation result. In addition, the determination unit  114  may specify a feature amount of a sample in the vicinity of a feature amount of a spoken sentence from the user based on the nearest neighbor method, and may specify an intention label to which the sample belongs. Incidentally, the determination unit  114  determines whether or not the user&#39;s spoken sentence is an invalid speech in the estimation result (second estimation result) of the search condition by a method similar to the method described in the first embodiment. 
     The storage unit  117  receives the determination result from the determination unit  114 , and stores the feature amount of the spoken sentence from the user in the sample memory unit  124  according to the type of the received determination result. Specifically, the storage unit  117  stores the feature amount of the spoken sentence from the user according to the second determination result (that is, a determination result indicating that the first estimation result from the first estimation unit  112  and the second estimation result from the second estimation unit  113  contradict each other.). In particular, the storage unit  117  stores the feature amount of the spoken sentence from the user in a case where it is estimated as “specifying search conditions” in the intention estimation, and no specific value is estimated for all the attributes in the search condition estimation. Alternatively, the storage unit  117  may store the feature amount of the spoken sentence from the user in a case where it is estimated as “specifying the search condition” in the intention estimation, and a specific value is specified in the search condition estimation, but the probability relevant to the value is lower than a preset threshold value. 
     The sample memory unit  124  stores a plurality of samples in which an intention label corresponding to each of a plurality of candidates regarding an intention is associated with a feature amount of a spoken sentence corresponding to the intention label. In other words, each sample is a pair in which one intention label and a feature amount of one spoken sentence are associated with each other. Each intention label is each intention assumed in advance, similar to an intention class. That is, the intention label is one of K+1 intention labels including K including “specifying search conditions”, “greeting”, “agreeing or disagreeing”, “operating the system”, and the like, and one “invalid speech”. An engineer who manages the spoken sentence processing apparatus  1  may extract a feature amount of a spoken sentence from each example by applying the intention estimation model  121  to a plurality of examples of spoken sentences collected in advance, and assign at least one of the extracted feature amounts to each intention label. 
       FIG.  9    is a flowchart showing an operation example of the spoken sentence processing apparatus  1  according to the third embodiment. Steps S 301  to S 304  according to the third embodiment are similar to steps S 101  to S 104  according to the first embodiment except for step S 302 . 
     In step S 302 , the first estimation unit  112  extracts a feature amount by applying the intention estimation model  121  to a spoken sentence from the user. Subsequently, the first estimation unit  112  calculates similarity between the extracted feature amount and the feature amount of at least one spoken sentence stored in the sample memory unit  124 . 
     In step S 305 , the storage unit  117  selects whether or not to store the user&#39;s spoken sentence based on the determination result of the determination unit  114 . When the storage unit  117  selects to store the spoken sentence (Yes in step S 305 ), the process proceeds to step S 306 . On the other hand, when the storage unit  117  selects not to store the spoken sentence (No in step S 305 ), the series of operations ends. 
     In step S 306 , the storage unit  117  stores the feature amounts of the user&#39;s spoken sentences in the sample memory unit  124 . After step S 306 , the spoken sentence processing apparatus  1  may end a series of operations and wait for reception of the next spoken sentence. 
     The spoken sentence processing apparatus  1  according to the third embodiment has been described above. The spoken sentence processing apparatus  1  according to the third embodiment determines whether or not a spoken sentence from a user is an invalid speech using each of an estimator based on intention estimation and an estimator based on search condition estimation. Specifically, the spoken sentence processing apparatus  1  extracts the feature amount of the spoken sentence by applying the intention estimation model  121  to the user&#39;s spoken sentence, and calculates the similarity between the extracted feature amount and the feature amount of at least one spoken sentence stored in the sample memory unit  124 . Subsequently, the spoken sentence processing apparatus  1  performs intention estimation based on the calculated similarity. Thereafter, the spoken sentence processing apparatus  1  stores the feature amount of the spoken sentence from the user according to the determination result. 
     According to the third embodiment, even if the intention estimation model  121  gives a high probability to an erroneous estimation result, it is possible to automatically correct an error in intention estimation based on an estimation result from a different viewpoint of search condition estimation. As the spoken sentence processing apparatus  1  is operated, the number of cases as described above increases and the number of samples increases, so that the number of types of invalid speeches that can be correctly recognized increases. As a result, the spoken sentence processing apparatus  1  can improve robustness of intention estimation. 
     Note that the spoken sentence processing apparatus  1  according to the third embodiment is applicable not only to the intention “specifying search conditions” exemplified above but also to other intentions (for example, greeting, agreeing or disagreeing, operating the system). For example, the spoken sentence processing apparatus  1  according to the third embodiment can be similarly applied to a case where the result of the intention estimation is “greeting”, “agreeing or disagreeing”, or “operating the system”, but the result of the search condition estimation does not match any of the previously-assumed values. 
     Furthermore, an engineer or the like who manages the spoken sentence processing apparatus  1  can improve the detection accuracy of the system by diverting the extracted feature amount of the invalid speech of the user to another spoken dialog system different from the spoken sentence processing apparatus  1 . 
     Furthermore, the spoken sentence processing apparatus  1  according to the third embodiment may further include the response generation unit  115  and the output unit  116  according to the second embodiment. The spoken sentence processing apparatus  1  according to the combination of the two embodiments can generate and output a response sentence based on the determination result while storing the feature amount of the spoken sentence from the user according to the determination result. 
       FIG.  10    is a block diagram showing a hardware configuration example of the spoken sentence processing apparatus  1  according to the first to third embodiments. The spoken sentence processing apparatus  1  includes a processing circuit  11 , a memory  12 , a display  13 , an input interface  14 , and a communication interface  15  as hardware. 
     The processing circuit  11  controls the operation of the spoken sentence processing apparatus  1 . The processing circuit  11  includes a processor such as a central processing unit (CPU), a micro processing unit (MPU), or a graphics processing unit (GPU) as hardware. The processing circuit  11  realizes each unit (for example, the reception unit  111 , first estimation unit  112 , second estimation unit  113 , determination unit  114 , response generation unit  115 , output unit  116 , and storage unit  117 ) corresponding to each program by executing each program expanded in the memory  12  via at least one processor. Note that each unit can be realized by the processing circuit  11  including a single processor or the processing circuit  11  combining a plurality of processors. 
     The memory  12  stores information such as data and programs used by the processing circuit  11 . The memory  12  includes a semiconductor memory element such as a random access memory (RAM) as hardware. The memory  12  may be a drive apparatus that reads and writes information from and to an external storage apparatus such as a magnetic disk (Floppy (registered trademark) disk, hard disk), a magneto-optical disk (MO), an optical disk (CD, DVD, Blu-ray (registered trademark)), a flash memory (USB flash memory, memory card, SSD), or a magnetic tape. Note that the storage area of the memory  12  may be in an internal or external storage apparatus of the spoken sentence processing apparatus  1 . Incidentally, the memory  12  may store the intention estimation model  121 , the search condition estimation model  122 , the response sentence template  123 , and the sample memory unit  124 . Furthermore, the memory  12  and the sample memory unit  124  may be configured separately from each other. 
     The display  13  displays information such as data generated by the processing circuit  11  and data stored in the memory  12 . As the display  13 , for example, a display such as a cathode ray tube (CRT) display, a liquid crystal display (LCD), a plasma display, an organic electroluminescence (OELD: Organic ElectroLuminescence Display) display, or a tablet terminal can be used. Incidentally, the display  13  may display a response sentence. 
     The input interface  14  receives an input from a user who uses the spoken sentence processing apparatus  1 , converts the received input into an electrical signal, and outputs the electrical signal to the processing circuit  11 . As the input interface  14 , physical operation components such as a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad, a touch panel display, and a microphone can be used. Note that the input interface  14  may be an apparatus that receives an input from an external input apparatus that is separate from the spoken sentence processing apparatus  1 , converts the received input into an electrical signal, and outputs the electrical signal to the processing circuit  11 . Incidentally, the input interface  14  may receive an input related to a predetermined threshold value or the like from an engineer who manages the spoken sentence processing apparatus  1 . 
     The communication interface  15  transmits and receives data to and from an external apparatus. Any communication standard can be used between the communication interface  15  and the external apparatus. Incidentally, the communication interface  15  may receive a spoken sentence from the user. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions.