Patent Publication Number: US-10789951-B2

Title: Speech continuation determination method, speech continuation determination device, and recording medium

Description:
BACKGROUND 
     1. Technical Field 
     The present disclosure relates to a speech continuation determination method, a speech continuation determination device, and a recording medium, and particularly relates to a speech continuation determination method, a speech continuation determination device, and a recording medium that determine user speech continuation by using a predetermined model obtained by machine learning. 
     2. Description of the Related Art 
     A speech continuation determination technology estimates whether a user intends to continue speech, in other words, whether user speech continues. Some disclosed technologies (for example, Japanese Patent No. 4667085) dynamically control the timing and content of response by a dialogue system by using the speech continuation determination technology. 
     In the technology disclosed in Japanese Patent No. 4667085, a wait time until inputting by, for example, user speech is received, or a voice reception time in which the inputting is continuously received is adjusted based on calculation of the maximum number of characters in each keyword included in user speech assumed for each question provided by the dialogue system. 
     SUMMARY 
     In one general aspect, the techniques disclosed here feature a speech continuation determination method including: acquiring dialogue data including a first sentence that is text data of a first speech sentence spoken to a user at a first time, a second sentence that is text data of second speech sentence spoken by the user at a second time following the first sentence, and structured data that is data obtained by structuring intention indicated by the first sentence; estimating a response delay amount indicating a wait time for a response to the second sentence by applying the dialogue data to a model on which learning result information is acquired by machine learning reflected; acquiring user status information indicating the status of the user; and determining whether a speech sentence by the user continues following the second sentence in accordance with the user status information in the wait time indicated by the response delay amount. 
     The accuracy of determining user speech continuation is improved by a speech continuation determination method according to the present disclosure. 
     These comprehensive and specific aspects may be achieved by a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM, or may be achieved by an optional combination of the system, the method, the integrated circuit, the computer program, and the recording medium. 
     Additional benefits and advantages of the disclosed embodiments will become apparent from the specification and drawings. The benefits and/or advantages may be individually obtained by the various embodiments and features of the specification and drawings, which need not all be provided in order to obtain one or more of such benefits and/or advantages. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an exemplary configuration of a speech continuation determination system according to Embodiment 1; 
         FIG. 2  is a block diagram illustrating another exemplary configuration of the speech continuation determination system according to Embodiment 1; 
         FIG. 3  is a diagram illustrating exemplary learning data according to Embodiment 1; 
         FIG. 4  is a block diagram illustrating a detailed exemplary configuration of a learning unit according to Embodiment 1; 
         FIG. 5  is a diagram illustrating exemplary dialogue data according to Embodiment 1; 
         FIG. 6  is a block diagram illustrating a detailed exemplary configuration of an estimation unit according to Embodiment 1; 
         FIG. 7  is a diagram illustrating an exemplary response delay estimation result according to Embodiment 1; 
         FIG. 8  is a diagram illustrating exemplary user status information according to Embodiment 1; 
         FIG. 9  is a block diagram illustrating a detailed exemplary configuration of a determination unit according to Embodiment 1; 
         FIG. 10  is a diagram illustrating an exemplary speech continuation determination result according to Embodiment 1; 
         FIG. 11  is a flowchart illustrating the outline of operation of the speech continuation determination system according to Embodiment 1; 
         FIG. 12  is a detailed flowchart of learning processing according to Embodiment 1; 
         FIG. 13  is a flowchart illustrating details of estimation processing illustrated in  FIG. 11 ; 
         FIG. 14  is a flowchart illustrating details of the determination processing illustrated in  FIG. 11 ; 
         FIG. 15A  is a diagram for description of an aspect of first determination according to Embodiment 1; 
         FIG. 15B  is a diagram for description of another aspect of the first determination according to Embodiment 1; 
         FIG. 15C  is a diagram for description of an aspect of second determination according to Embodiment 1; and 
         FIG. 16  is a block diagram illustrating an exemplary configuration of a dialogue system according to Embodiment 2. 
     
    
    
     DETAILED DESCRIPTION 
     (Underlying Knowledge Forming Basis of the Present Disclosure) 
     A speech continuation determination technology estimates whether a user intends to continue speech, in other words, whether user speech continues. The speech continuation determination technology is used to dynamically control the timing and content of response by a dialogue system. However, when the accuracy of the determination is poor, the dialogue system wrongly responds the same content or the content of a next question while the user intends to continue speech, which results in miss of the user speech. The wrong response leads to increase in the time of dialogue between the dialogue system and the user. Thus, the accuracy of the user speech continuation determination has been desired to be improved. 
     For example, Japanese Patent No. 4667085 discloses the technology of adjusting a wait time until voice reception and/or a voice reception time in which voice is continuously received based on a result of voice recognition grammar analysis on each question in accordance with a rule to use the maximum number of keyword characters assumed for the question. This technology can maintain smooth dialogue without generating a wait time (voice recognition) beyond a necessary duration for the user. 
     The technology disclosed in Japanese Patent No. 4667085 assumes that the maximum and minimum numbers of keywords to be received by the system all at once is naturally determined depending on a question. However, when free speech expression by the user is allowed, the number of keywords is zero or no maximum number is determined due to parallel speech of keywords in some cases, which makes it difficult to appropriately determine the wait time. 
     Thus, the above-described method disclosed in Japanese Patent No. 4667085 cannot perform the user speech continuation determination at a sufficient accuracy. 
     Accordingly, speech continuation needs to be determined the meaning of user speech content with taken into consideration to improve the accuracy of the user speech continuation determination. 
     The present disclosure is intended to solve the above-described problem by providing a speech continuation determination method, a speech continuation determination device, and a recording medium storing a computer program that improve the accuracy of user speech continuation determination. 
     An aspect of the present disclosure is a speech continuation determination method including: acquiring dialogue data including a first sentence that is text data of a first speech sentence spoken to a user at a first time, a second sentence that is text data of second speech sentence spoken by the user at a second time following the first sentence, and structured data that is data obtained by structuring intention indicated by the first sentence; estimating a response delay amount indicating a wait time for a response to the second sentence by applying the dialogue data to a model on which learning result information is acquired by machine learning reflected; acquiring user status information indicating the status of the user; and determining whether a speech sentence by the user continues following the second sentence in accordance with the user status information in the wait time indicated by the response delay amount. 
     According to the present aspect, the response delay amount for a user speech sentence is estimated by using a result of the learning processing, and whether user speech continues is determined by using the user status in a wait time indicated by the estimated response delay amount. In this manner, the accuracy of the user speech continuation determination is improved by performing the user speech continuation determination at two stages. 
     Preferably, in the aspect, the user status information indicates a value corresponding to at least one of (1) whether a text input field at a text input unit for inputting speech by the user in text is being selected, (2) whether a voice signal is being input at a voice input unit for inputting speech by the user by sound, (3) whether a position of the sight line of the user at a sight line detection unit for detecting the sight line of the user is on a device including a presentation unit presenting the first sentence, and (4) whether face and body directions of the user at a posture detection unit for detecting a posture of the user point to a device including a presentation unit presenting the first sentence. 
     Preferably, in the aspect, when the wait time indicated by the response delay amount is zero, the determining performs first determination indicating that the speech sentence by the user does not continue, and while the wait time indicated by the response delay amount is larger than zero, the determining performs second determination indicating that the speech sentence by the user continues when the user status information indicates a predetermined value in the wait time, or performs the first determination when the user status information does not indicate the predetermined value in the wait time. 
     Preferably, in the aspect, the speech continuation determination method further includes acquiring learning data including a third sentence that is text data of a third speech sentence spoken to the user, a fourth sentence that is text data of a fourth speech sentence spoken by the user following the third sentence, structured data that is data obtained by structuring intention indicated by the third sentence, and a response delay amount indicating a wait time for a response to the fourth sentence; causing the model to perform learning by using the learning data; and storing a result of the learning at the learning step as the learning result information, in which the learning causes the model to learn correspondence between the fourth sentence and the response delay amount by using, as teacher data, the response delay amount included in the learning data. 
     According to the present aspect, learning data including system intention of a past system speech sentence and a user speech sentence in response to the past system speech sentence is used to perform learning processing of learning the correspondence between the user speech sentence included in the learning data and a response delay amount for the user speech sentence by using the response delay amount as teacher data. Accordingly, the response delay amount is accurately estimated from the user speech sentence following the system speech sentence. 
     Preferably, in the aspect, the model includes: a first model that calculates a first feature vector of features such as a word included in a word string acquired through morpheme analysis on the fourth sentence, and a dialogue action and a keyword included in the structured data, and outputs a first characteristic vector representing a dialogue state of the fourth sentence based on the calculated first feature vector and a first weight parameter; and a second model that outputs a response delay amount for the fourth sentence based on the first characteristic vector and a second weight parameter, in which the learning causes the model to perform learning by using the learning data by updating the first weight parameter and the second weight parameter by an error backpropagation method based on error between the response delay amount output by the second model and the response delay amount included in the learning data. 
     Preferably, in the aspect, the first model includes a neural network that has the first weight parameter and calculates the first characteristic vector from the calculated first feature vector, and the second model is a neural network model that outputs sigmoid regression, a support vector machine (SVM) model, or a naive Bayes classifier. 
     Preferably, in the aspect, the learning acquires the learning data from a corpus accumulating a speech sentence spoken to a user, structured data of intention indicated by the speech sentence, a speech sentence spoken by the user following the speech sentence, and a response delay amount for the speech sentence spoken by the user. 
     Preferably, in the aspect, the intention indicated by the first sentence includes a dialogue action indicating the kind of the intention of the first sentence and obtained by classifying the first sentence based on categories and actions, and a keyword included in the first sentence. 
     Preferably, in the aspect, the intention indicated by the third sentence includes a dialogue action indicating the kind of the intention of the third sentence and obtained by classifying the third sentence based on categories and actions, and a keyword included in the third sentence. 
     An aspect of the present disclosure is a speech continuation determination device including: a dialogue acquisition unit that acquires dialogue data including a first sentence that is text data of a first speech sentence spoken to a user at a first time, a second sentence that is text data of second speech sentence spoken by the user at a second time following the first sentence, and structured data that is data obtained by structuring intention indicated by the first sentence; an estimation unit that estimates a response delay amount indicating a wait time for a response to the second sentence by applying the dialogue data to a model on which learning result information is acquired by machine learning reflected; a status acquisition unit that acquires user status information indicating the status of the user; and a determination unit that determines whether a speech sentence by the user continues following the second sentence in accordance with the user status information in the wait time indicated by the response delay amount. 
     According to the present aspect, the response delay amount for the user speech sentence is estimated by using a result of the learning processing, and whether user speech continues is determined by using a user status in a wait time indicated by the estimated response delay amount. In this manner, the accuracy of the user speech continuation determination is improved by performing the user speech continuation determination at two stages. 
     These comprehensive and specific aspects may be achieved by a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM, or may be achieved by an optional combination of the system, the method, the integrated circuit, the computer program, and the recording medium. 
     Embodiments of the present disclosure will be described below with reference to the accompanying drawings. The embodiments to be described below each indicate a specific example of the present disclosure. For example, numerical values, shapes, components, steps, and orders of the steps, which are indicated in the embodiments described below are merely exemplary and not intended to limit the present disclosure. Among components in the embodiments described below, any component not recited in an independent claim representing a highest-level concept is described as an optional component. Contents in all embodiments may be combined with each other. 
     Embodiment 1 
     The following describes a speech continuation determination method and the like in Embodiment 1 with reference to the accompanying drawings. 
     [Speech Continuation Determination System] 
       FIG. 1  is a block diagram illustrating an exemplary configuration of a speech continuation determination system  1  according to Embodiment 1.  FIG. 2  is a block diagram illustrating another exemplary configuration of a speech continuation determination system  1 A according to Embodiment 1. Any element identical to that in  FIG. 1  is denoted by an identical reference sign. 
     The speech continuation determination system  1  illustrated in  FIG. 1  includes a storage device  10 , a learning processing device  11 , and a speech continuation determination device  12 . 
     &lt;Storage Device  10 &gt; 
     The storage device  10  stores a corpus  101  and dialogue data  102 . The corpus  101  is a database obtained by accumulating a large volume of text data representing speech in dialogue. In the present embodiment, the corpus  101  includes a plurality of pieces of learning data  1011 . The learning data  1011  is used by the learning processing device  11  when performing learning processing. 
     The storage device  10  is achieved by non-transitory rewritable memory such as a hard disk drive or a solid state drive. 
     &lt;Learning Processing Device  11 &gt; 
     The learning processing device  11  acquires at least one piece of the learning data  1011  from the corpus  101  stored in the storage device  10  and performs learning processing of a predetermined model for estimating a response delay amount indicating a wait time for a response to speech by a user. The learning processing device  11  is achieved by, for example, a computer. 
     &lt;Speech Continuation Determination Device  12 &gt; 
     The following describes the configuration of the speech continuation determination device  12  according to the present embodiment. 
     The speech continuation determination device  12  acquires the dialogue data  102  from the storage device  10  and determines speech continuation by the user, in other words, whether the user desires to continue speech, by using a predetermined model obtained by machine learning by the learning processing device  11 . The speech continuation determination device  12  is achieved by, for example, a computer. 
     The speech continuation determination system  1  according to Embodiment 1 is not limited to the configuration illustrated in  FIG. 1 . As illustrated in the speech continuation determination system  1 A in  FIG. 2 , the speech continuation determination device  12  may include a learning processing device unit  11 A that is a functional component corresponding to the learning processing device  11  illustrated in  FIG. 1 , and a speech continuation determination device unit  12 A that is a functional component corresponding to the speech continuation determination device  12  illustrated in  FIG. 1 . Accordingly, the learning processing and the speech continuation determination processing may be both performed by the speech continuation determination device  12  illustrated in  FIG. 2 , or may be performed by different devices such as the learning processing device  11  and the speech continuation determination device  12 , respectively, as illustrated in  FIG. 1 . The speech continuation determination device  12  illustrated in  FIG. 2  may further include the storage device  10 . 
     The following describes detailed configurations of the learning processing device  11  and the speech continuation determination device  12 . 
     [Learning Processing Device  11 ] 
     As illustrated in, for example,  FIG. 1 , the learning processing device  11  includes an acquisition unit  111 , a learning unit  112 , and a storage unit  113 . 
     &lt;Storage Unit  113 &gt; 
     The storage unit  113  stores, as learning result information  1131 , a result of learning by the learning unit  112 . The storage unit  113  is achieved by a non-transitory rewritable memory such as a hard disk drive or a solid state drive. 
     &lt;Acquisition Unit  111 &gt; 
     The acquisition unit  111  acquires learning data including a third sentence that is text data of a third speech sentence spoken to the user, a fourth sentence that is text data of a fourth speech sentence spoken by the user following the third sentence, structured data that is data obtained by structuring intention indicated by the third sentence, and a response delay amount indicating a wait time for a response to the fourth sentence. 
     The acquisition unit  111  acquires the learning data from the corpus  101  accumulating a speech sentence spoken to a user, structured data of intention indicated by the speech sentence, a speech sentence spoken by the user following the speech sentence, and a response delay amount for the speech sentence spoken by the user. 
     In the present embodiment, the acquisition unit  111  acquires at least one piece of the learning data  1011  from the corpus  101  stored in the storage device  10 . The acquisition unit  111  is achieved by, for example, a processor such as a CPU, an ASIC, or an FPGA, and achieved by a processor such as a CPU executing a computer-readable program held in a computer. 
       FIG. 3  is a diagram illustrating exemplary learning data  1011  according to Embodiment 1. As described above, the learning data  1011  illustrated in  FIG. 3  is included in the corpus  101 . In other words, the corpus  101  corresponds to accumulation of the learning data  1011 . 
     The learning data  1011  includes a plurality of pieces of speech data each indicating a speech sentence by the user in response to a speech sentence by a system. The system may be the speech continuation determination system  1  or a dialogue system, a server, a device, or the like that generates dialogue or a response.  FIG. 3  illustrates the learning data  1011  including a plurality of pieces of speech data of dialogue about meal advisement in Japanese. 
     As illustrated in  FIG. 3 , the learning data  1011  includes system intention  143 , a system sentence  144 , a user sentence  145 , and a response delay amount  146 , to which a data ID  142  is provided. The learning data  1011  includes training data  141  including at least one set of the system intention  143  to the response delay amount  146  to which the data ID  142  is provided. In the example illustrated in  FIG. 3 , the training data  141  includes the system intention  143  to the response delay amount  146  to which the data ID  142  indicating “1” is provided, but the present disclosure is not limited thereto. 
     The data ID  142  is an identifier for uniquely specifying the training data  141 . The data ID  142  is used to uniquely specify the contents of the system intention  143  to the response delay amount  146 . 
     The system sentence  144  is text data of one speech sentence indicating a sentence in one piece of speech by the system. More specifically, the system sentence  144  is text data of a speech sentence spoken to the user by the system, and an exemplary third sentence. The user sentence  145  is text data of one speech sentence indicating a sentence in one piece of speech by the user. More specifically, the user sentence  145  is text data of a speech sentence spoken by the user following the system sentence  144 , and is an exemplary fourth sentence. The system sentence  144  ad the user sentence  145  are each string data of Japanese words without a delimiter. In a case of English text data, the system sentence  144  and the user sentence  145  may be each string data of English words with a delimiter of a half-width space. 
     The system intention  143  is data (structured data) obtained by structuring intention indicated by the system sentence  144 . The structured data includes a dialogue action that indicates the kind of intention of a sentence indicated by the system sentence  144  and is obtained by classifying the sentence based on categories and actions, and a keyword included in the sentence indicated by the system sentence  144 . Thus, it can be regarded that the system intention  143  indicates the meaning or outline of the system sentence  144 . In the example illustrated in  FIG. 3 , the system intention  143  includes the dialogue action of the system sentence  144  such as “YES/NO question”, “check question”, or “open question”, and a keyword such as &lt;Kaisendon&gt; or &lt;Chicken&gt;. 
     In an early phase of dialogue between the system and the user, the system intention  143  and the system sentence  144 , which would be otherwise empty, are provided with “INIT”, which is a string indicating an initial state. 
     The response delay amount  146  indicates whether there is a wait time for a response to the user sentence  145 . At learning, the response delay amount  146  is used as teacher data for the user sentence  145  following the system sentence  144 . As illustrated in  FIG. 3 , the response delay amount  146  is, for example, zero or one, but a fixed value (for example, five seconds) corresponding to each value (zero or one) may be used in actual delay control. The response delay amount  146  may be a numerical value indicating how long a response of the system to the user sentence  145  is to be delayed. 
     In the example illustrated in  FIG. 3 , when the training data  141 , the data ID  142  of which indicates “1” is a target of the learning processing by the learning unit  112 , the acquisition unit  111  acquires the system intention  143  of “YES/NO question &lt;Kaisendon&gt;”, the system sentence  144  of “Do you want to eat Kaisendon?”, and the user sentence  145  of “I had fish food yesterday,”. Then, the acquisition unit  111  acquires “1” indicated as the response delay amount  146 , the data ID  142  of which indicates “1”, as a true value, in other words, teacher data at learning by using the training data  141 . 
     The acquisition unit  111  acquires the training data  141  with a different value of the data ID  142  at each learning performed by the learning unit  112  until the learning converges. The acquisition unit  111  may acquire the training data  141  having a different value of the data ID  142  by incrementing the data ID  142 , or may acquire any other training data  141 . 
     &lt;Learning Unit  112 &gt; 
     The learning unit  112  causes a predetermined model to perform learning by using the learning data  1011 . More specifically, the learning unit  112  causes the predetermined model to learn correspondence between the user sentence  145  (fourth sentence) and the response delay amount  146  by using, as teacher data, the response delay amount  146  included in the learning data  1011 . More specifically, in description with reference to  FIG. 3 , the learning unit  112  calculates a first characteristic vector indicating a dialogue state based on structured data indicated by a dialogue action and a keyword included in the system intention  143  and a word included in the user sentence  145  by applying the learning data  1011  acquired by the acquisition unit  111  to the predetermined model. Then, the correspondence between the user sentence  145  and the response delay amount  146  is learned by using the calculated first characteristic vector and a weight parameter. Then, the learning unit  112  stores a result of the learning as the learning result information  1131  in the storage unit  113 . 
     In the present embodiment, the predetermined model includes a first model including a first weight parameter, and a second model including a second weight parameter. The first model calculates a first feature vector of features such as a word included in a word string acquired through morpheme analysis on the user sentence  145  (fourth sentence), and a dialogue action and a keyword included in the structured data of the system sentence  144  (third sentence), and outputs the first characteristic vector representing the dialogue state of the user sentence  145  (fourth sentence) based on the calculated first feature vector and the first weight parameter. For example, the first model includes a neural network that has the first weight parameter and calculates the first characteristic vector from the calculated first feature vector. 
     The second model outputs a response delay amount for the user sentence  145  (fourth sentence) based on the first characteristic vector and the second weight parameter. For example, the second model employs a neural network model that outputs sigmoid regression, a support vector machine (SVM) model, or a naive Bayes classifier. 
     The learning unit  112  causes the predetermined model to perform learning by using the learning data  1011  by updating the first weight parameter and the second weight parameter by an error backpropagation method based on error between the response delay amount output from the second model and the response delay amount  146  included in the learning data  1011 . The learning unit  112  is achieved by, for example, a processor such as a CPU, an ASIC, or an FPGA, and achieved by a processor such as a CPU executing a computer-readable program held in a computer. 
       FIG. 4  is a block diagram illustrating a detailed exemplary configuration of the learning unit  112  according to Embodiment 1. 
     As illustrated in  FIG. 4 , the learning unit  112  includes a morpheme analysis unit  1121 , a feature vector calculation unit  1122 , a dialogue state calculation unit  1123 , a response delay estimation unit  1124 , and a weight update unit  1125 . 
     &lt;&lt;Morpheme Analysis Unit  1121 &gt;&gt; 
     The morpheme analysis unit  1121  analyzes each morpheme, which is the minimum unit having meaning in natural language, in the user sentence  145  acquired by the acquisition unit  111 , and converts the user sentence  145  into a word string including the morpheme as a word. 
     In the present embodiment, the morpheme analysis unit  1121  performs the conversion to a word string by dividing, into words, each user sentence  145  acquired by the acquisition unit  111 . When the user sentence  145  is in Japanese, the morpheme analysis unit  1121  can achieve this processing by using a morpheme analysis application such as MeCab. For example, the morpheme analysis unit  1121  divides the user sentence  145  of “I had fish food yesterday, (kinousakanawotabetanode,)” into “kinou”, “sakana”, “wo”, “tabe”, “ta”, “node”, and “,”. When the user sentence  145  is in English, the morpheme analysis unit  1121  can achieve this processing by using a half-width space as a word delimiter. 
     &lt;&lt;Feature Vector Calculation Unit  1122 &gt;&gt; 
     The feature vector calculation unit  1122  converts a word string obtained by the morpheme analysis unit  1121 , and a dialogue action and a keyword included in the system intention  143  into a feature vector that is a sentence vector expression in which the word string, the dialogue, and the keyword are expressed as features. 
     The conversion into a feature vector may be performed by a method using a Bag-of-words model that considers only whether any word is included in a sentence but not the order of words or the like. When the Bag-of-words model is used, the conversion into a feature vector can be achieved by, for example, expressing an element value as one only when each feature in a list (hereinafter also referred to as a dictionary) of all features assumed as inputs exists. Accordingly, the feature vector includes, as elements, all words, dialogue actions, and keywords included in the dictionary, which are each expressed as one or zero. 
     &lt;&lt;Dialogue State Calculation Unit  1123 &gt;&gt; 
     The dialogue state calculation unit  1123  calculates, by using the first model included in the predetermined model, the first characteristic vector indicating a dialogue state from the feature vector calculated by the feature vector calculation unit  1122 . 
     In the present embodiment, the dialogue state calculation unit  1123  calculates, by using the first model, the first characteristic vector indicating a dialogue state based on the feature vector calculated by the feature vector calculation unit  1122  and the first weight parameter. The first model may be one neural network model that outputs sigmoid regression and can calculate the first characteristic vector from the first feature vector. 
     &lt;&lt;Response Delay Estimation Unit  1124 &gt;&gt; 
     The response delay estimation unit  1124  estimates a response delay amount for the user sentence  145  from the first characteristic vector calculated by the dialogue state calculation unit  1123  by using the second model included in the predetermined model. 
     In the present embodiment, the response delay estimation unit  1124  calculates a posterior probability indicating probability distribution of the response delay amount (zero or one) for the user sentence  145  based on the first characteristic vector calculated by the dialogue state calculation unit  1123  and the second weight parameter by using the second model. 
     The second model is a probability model such as a sigmoid regression model, an SVM model, or a naive Bayes classifier. The second model may be one neural network model that outputs sigmoid regression and can estimate the response delay amount from the first characteristic vector. 
     &lt;&lt;Weight Update Unit  1125 &gt;&gt; 
     The weight update unit  1125  updates the first weight parameter and the second weight parameter of the predetermined model based on error between the response delay amount estimated by the response delay estimation unit  1124  and the response delay amount  146  included in the learning data  1011 . 
     In description with reference to  FIG. 3 , the weight update unit  1125  updates the first weight parameter and the second weight parameter of the predetermined model to appropriate numerical values by the error backpropagation method based on the posterior probability of the response delay amount calculated by the response delay estimation unit  1124  in the training data  141  as a learning target and the true value indicated as the response delay amount  146  to which the data ID  142  included in the training data  141  is provided. In other words, the weight update unit  1125  updates the first weight parameter and the second weight parameter so that prediction error between the posterior probability and the true value is minimized. The weight update unit  1125  may determine that the prediction error is minimized and end the update when the prediction error is not further reduced. 
     In this manner, the learning unit  112  executes learning processing by performing learning to minimize the error between the posterior probability of the calculated response delay amount and the value indicated by the response delay amount  146  as a true value to which the data ID  142  indicated by the training data  141  is provided. 
     The learning unit  112  stores the first weight parameter and the second weight parameter (learning parameters) at the end of the learning processing as the learning result information  1131  in the storage unit  113 . 
     [Speech Continuation Determination Device  12 ] 
     The following describes a detailed configuration of the speech continuation determination device  12 . 
     As illustrated in, for example,  FIG. 1 , the speech continuation determination device  12  includes a dialogue acquisition unit  121 , an estimation unit  122 , a storage unit  123 , a status acquisition unit  124 , and a determination unit  125 . 
     &lt;Storage Unit  123 &gt; 
     The storage unit  123  stores a response delay estimation result  1231 , user status information  1232 , and a speech continuation determination result  1233 . The storage unit  123  is achieved by a non-transitory rewritable memory such as a hard disk drive or a solid state drive. The user status information  1232  and the speech continuation determination result  1233  will be described later in detail. 
     &lt;Dialogue Acquisition Unit  121 &gt; 
     The dialogue acquisition unit  121  acquires dialogue data including a first sentence that is text data of a first speech sentence spoken to a user at a first time, a second sentence that is text data of a second speech sentence spoken by the user at a second time following the first sentence, and structured data that is data obtained by structuring intention indicated by the first sentence. 
     In the present embodiment, the dialogue acquisition unit  121  acquires the dialogue data  102  stored in the storage device  10 . The dialogue data  102  includes a plurality of sets of a system speech sentence and a user speech sentence indicating a response to the speech sentence. The dialogue acquisition unit  121  is achieved by, for example, a processor such as a CPU, an ASIC, or an FPGA, and achieved by a processor such as a CPU executing a computer-readable program held in a computer. 
       FIG. 5  is a diagram illustrating exemplary dialogue data  102  according to Embodiment 1. The dialogue data  102  illustrated in  FIG. 5  includes speech data representing dialogue about meal advisement in Japanese. 
     As illustrated in  FIG. 5 , the dialogue data  102  includes system intention  183 , a system sentence  184 , and a user sentence  185 , to which a data ID  182  is provided. The dialogue data  102  includes speech data  181  including at least one set of the system intention  183  to the user sentence  185 , to which the data ID  182  is provided. In other words, the speech data  181  is data that is a speech continuation determination target included in the dialogue data  102 . In the example illustrated in  FIG. 5 , the speech data  181  includes the system intention  183 , the system sentence  184 , and the user sentence  185 , the data ID  182  of which is “1”. 
     The data ID  182  is an identifier for uniquely specifying the speech data  181 . The data ID  182  is used to uniquely specify the contents of the system intention  183  to the user sentence  185 . 
     Similarly to the system sentence  144 , the system sentence  184  is text data of one speech sentence indicating a sentence in one piece of speech by the system. More specifically, the system sentence  184  is text data of a speech sentence spoken to the user by the system, and is an exemplary first sentence. Similarly to the user sentence  145 , the user sentence  185  is text data of one speech sentence indicating a sentence in one piece of speech by the user. More specifically, the user sentence  185  is text data of a speech sentence spoken by the user following the system sentence  184 , and is an exemplary second sentence. 
     Similarly to the system intention  143 , the system intention  183  is data (structured data) obtained by structuring intention indicated by the system sentence  184 . 
     The dialogue data  102  is generated based on a plurality of externally input speech sentences. For example, first, the system sentence  184  output from the system, the system intention  183  associated with the system sentence  184 , and the user sentence  185  input in response to the system sentence  184  are obtained. Subsequently, the data ID  182  is uniquely provided to the system intention  183 , the system sentence  184 , and the user sentence  185 . The unit of a plurality of speech sentences, in other words, one piece of speech is, for example, text transmitted to a receiver all at once when inputting is made through a text chat system, or a result of recognition up to generation of a continuous silent interval, which serves as trigger for voice recognition, when inputting is made through a voice dialogue system. 
     Although the dialogue data  102  is stored in the storage device  10  outside of the speech continuation determination device  12 , the present disclosure is not limited thereto. The dialogue data  102  may be generated by the speech continuation determination device  12  based on an externally input user speech sentence. In other words, the dialogue data  102  may be generated by the speech continuation determination device  12  or another device. 
     In the example illustrated in  FIG. 5 , when the speech continuation determination target is the speech data  181 , the data ID  182  of which indicates, for example, “1”, the dialogue acquisition unit  121  acquires the system intention  183  of “YES/NO question &lt;Chinese food&gt;”, the system sentence  184  of “Do you want to eat Chinese food?”, and the user sentence  185  of “I&#39;ve just had Chinese food,”. In other words, the dialogue acquisition unit  121  acquires, as the dialogue data  102 , the speech data  181  that is a speech continuation determination target, in other words, the system sentence  184  at a first time, the user sentence  185  spoken by the user following the system sentence  184  at a second time following the first time, and structured data that is data obtained by structuring intention indicated by the system sentence. 
     &lt;Estimation Unit  122 &gt; 
     The estimation unit  122  estimates the response delay amount indicating a wait time for a response to the second sentence by applying the dialogue data to the predetermined model on which the learning result information  1131  acquired by machine learning by the learning processing device  11  is reflected. 
     More specifically, the estimation unit  122  estimates the response delay amount indicating a wait time for the user sentence  185  as an estimation target by using the predetermined model on which the learning result information  1131  stored in the storage unit  113  is reflected. The predetermined model has a structure identical to that of the model used by the learning unit  112 . The estimation unit  122  stores the estimated response delay amount as the response delay estimation result  1231  in the storage unit  123 . 
     The estimation unit  122  is achieved by, for example, a processor such as a CPU, an ASIC, or an FPGA, and is achieved by a processor such as a CPU executing a computer-readable program held in a computer. 
       FIG. 6  is a block diagram illustrating a detailed exemplary configuration of the estimation unit  122  according to Embodiment 1. Any element identical to that in  FIG. 1  or any other drawing is denoted by an identical reference sign. 
     In the present embodiment, as illustrated in  FIG. 6 , the estimation unit  122  includes a morpheme analysis unit  1221 , a feature vector calculation unit  1222 , a dialogue state calculation unit  1223 , a response delay estimation unit  1224 , and a response delay determination unit  1225 . 
     &lt;&lt;Morpheme Analysis Unit  1221 &gt;&gt; 
     The morpheme analysis unit  1221  converts the user sentence  185  into a word string by analyzing morphemes of the user sentence  185  included in the dialogue data  102  (speech data  181 ) acquired by the dialogue acquisition unit  121  and dividing the user sentence  185  into words that are the morphemes. 
     The morpheme analysis is performed by the above-described method. 
     &lt;&lt;Feature Vector Calculation Unit  1222 &gt;&gt; 
     The feature vector calculation unit  1222  converts the word string obtained through the conversion at the morpheme analysis unit  1221 , and a dialogue action and a keyword included in the system intention  143  into a feature vector that is a sentence vector expression in which the word string, the dialogue action, and the keyword are expressed as features. 
     The feature vector conversion is performed by the above-described method. 
     &lt;&lt;Dialogue State Calculation Unit  1223 &gt;&gt; 
     The dialogue state calculation unit  1223  calculates, by using the first model included in the predetermined model subjected to learning, the first characteristic vector indicating a dialogue state from the feature vector calculated by the feature vector calculation unit  1222  and the like. In the present embodiment, the dialogue state calculation unit  1223  reads the learning result information  1131  stored in the storage unit  123 , reflects the learning result information  1131  onto the first weight parameter of the first model, and converts the learning result information  1131  into the first characteristic vector indicating a dialogue state based on the feature vector calculated by the feature vector calculation unit  1222  and the first weight parameter subjected to learning. 
     &lt;&lt;Response Delay Estimation Unit  1224 &gt;&gt; 
     The response delay estimation unit  1224  estimates the response delay amount for the user sentence  185  from the first characteristic vector calculated by the dialogue state calculation unit  1223  and the like by using the second model included in the predetermined model subjected to learning. In the present embodiment, the response delay estimation unit  1224  reads the learning result information  1131  stored in the storage unit  123 , reflects the learning result information  1131  onto the first weight parameter of the first model, and calculates the posterior probability indicating the probability distribution of the response delay amount (zero or one) for the user sentence  185  based on the first characteristic vector calculated by the dialogue state calculation unit  1223  and the second weight parameter subjected to learning. 
     &lt;&lt;Response Delay Determination Unit  1225 &gt;&gt; 
     The response delay determination unit  1225  determines a predicted response delay amount based on the posterior probability of the response delay amount, which is estimated by the response delay estimation unit  1224 . The response delay determination unit  1225  stores the determined predicted response delay amount as the response delay estimation result  1231  in the storage unit  123 . For example, the response delay determination unit  1225  determines the predicted response delay amount to be a higher probability value among probability values of the response delay amount indicated by the posterior probability, and stores the probability value as the response delay estimation result  1231  in the storage unit  123 . 
       FIG. 7  is a diagram illustrating an exemplary response delay estimation result  1231  according to Embodiment 1. Any element identical to that in  FIG. 5  is denoted by an identical reference sign. 
     As illustrated in  FIG. 7 , the response delay estimation result  1231  includes an estimation result  191  indicating a predicted response delay amount  192  for each user sentence  185  included in the dialogue data  102  illustrated in  FIG. 5 . In other words, each estimation result  191  includes the system intention  183 , the system sentence  184 , the user sentence  185 , and the predicted response delay amount  192 , to which the data ID  182  is provided. The predicted response delay amount  192  is determined by the response delay determination unit  1225 . For example, in  FIG. 7 , in the estimation result  191 , the data ID  182  of which indicates “1”, the predicted response delay amount  192  of “1” is indicated for the system intention  183  of “YES/NO question &lt;Chinese food&gt;”, the system sentence  184  of “Do you want to eat Chinese food?”, and the user sentence  185  of “I&#39;ve just had Chinese food,”. 
     &lt;Status Acquisition Unit  124 &gt; 
     The status acquisition unit  124  acquires the user status information  1232  indicating a user status. In the present embodiment, the status acquisition unit  124  acquires at least the user status information  1232  included in a time until a wait time elapses, and stores the user status information  1232  in the storage unit  123 . The user status information  1232  is used in speech continuation determination by the determination unit  125  to be described later. 
       FIG. 8  is a diagram illustrating exemplary user status information  1232  according to Embodiment 1. As illustrated in  FIG. 8 , the user status information  1232  includes a data ID  220 , a time  221 , and a user status  222 . 
     The data ID  220  indicates a correspondence relation with the data ID  182  illustrated in  FIG. 7 . An item with the number of “1” indicates the user status in the wait time for the data ID  182  indicating “1” in  FIG. 7 . The time  221  indicates an incremented time for the data ID  220  indicating the same number. In the example illustrated in  FIG. 8 , the time  221  indicates a time incremented at each second when the wait time is five seconds. The user status  222  indicates the user status at a time indicated by the time  221 . The user status  222  indicates whether a text input field at a text input unit for inputting user speech by text is being selected. 
     The user status  222  does not necessarily need to indicate whether the text input field is being selected. The user status  222  may indicate whether a voice signal is being input at a voice input unit for inputting user speech by sound, or may indicate whether the position of the user sight line at a sight line detection unit for detecting a user sight line is on a device including a presentation unit presenting the system sentence  184  (first sentence). Alternatively, the user status  222  may indicate whether user face and body directions at a posture detection unit for detecting a user posture point to the device including the presentation unit presenting the system sentence  184  (first sentence). The user status information  1232  indicates a value corresponding to a sentence indicating the user status  222  as illustrated in  FIG. 8 . The value may indicate whether the user is performing an operation or a response for speech continuation, and may be, for example, zero or one. The user status information  1232  may be expressed in a sentence indicating the user status  222 . 
     &lt;Determination Unit  125 &gt; 
     The determination unit  125  determines whether a user speech sentence continues following the user sentence  185  (second sentence) in accordance with the user status information  1232  in a wait time indicated by the predicted response delay amount  192 . More specifically, when the wait time indicated by the predicted response delay amount  192  is zero, the determination unit  125  may perform first determination indicating that the user speech sentence does not continue. While the wait time indicated by the predicted response delay amount  192  is larger than zero, the determination unit  125  performs second determination indicating that the user speech sentence continues when the user status information in the wait time indicates a predetermined value, or performs the above-described first determination when the user status information in the wait time does not indicate the predetermined value. 
       FIG. 9  is a block diagram illustrating a detailed exemplary configuration of the determination unit  125  according to Embodiment 1. Any element identical to that in  FIG. 1  or any other drawing is denoted by an identical reference sign. 
     In the present embodiment, as illustrated in  FIG. 9 , the determination unit  125  includes a wait time generation unit  1251  and an integration determination unit  1252 . The determination unit  125  is achieved by, for example, a processor such as a CPU, an ASIC, or an FPGA, and is achieved by a processor such as a CPU executing a computer-readable program held in a computer. 
     &lt;&lt;Wait Time Generation Unit  1251 &gt;&gt; 
     The wait time generation unit  1251  acquires the response delay estimation result  1231  stored in the storage unit  123 , and delays outputting of the system sentence  184  following the user sentence  185  in accordance with whether there is a wait time indicated by the response delay estimation result  1231 . For example, when the predicted response delay amount indicated by the response delay estimation result  1231  is “1”, the wait time generation unit  1251  generates a command for transition to a “waiting state” and outputs the command to a system that outputs a system sentence. Accordingly, the wait time generation unit  1251  controls the system not to perform outputting of the system sentence  184  following the user sentence  185  until a predetermined wait time, for example, five seconds elapses. 
     &lt;&lt;Integration Determination Unit  1252 &gt;&gt; 
     The integration determination unit  1252  determines whether user speech continues based on whether there is a wait time indicated by the response delay estimation result  1231  estimated by the estimation unit  122  and the user status  222  indicated by the user status information  1232  in the wait time. 
     For example, when the wait time indicated by the response delay estimation result  1231  is zero (no wait time), the integration determination unit  1252  determines that the user speech does not continue (first determination), and stores fragment information (also referred to as a speech discontinuation flag) indicating that the user speech does not continue as the speech continuation determination result  1233  in the storage unit  123 . 
     For example, when the wait time indicated by the response delay estimation result  1231  is larger than one or zero (in other words, when the wait time is non zero), the integration determination unit  1252  further acquires the user status information  1232  in the wait time, which is stored in the storage unit  123 . Then, when the user status information  1232  in the wait time indicates a predetermined value, the integration determination unit  1252  determines that the user speech continues (second determination), and stores fragment information (also referred to as a speech continuation flag) indicating that the user speech continues as the speech continuation determination result  1233  in the storage unit  123 . When the user status information  1232  in the wait time does not indicate the predetermined value, the integration determination unit  1252  determines that the user speech does not continue (first determination), and stores the speech discontinuation flag as the speech continuation determination result  1233  in the storage unit  123 . 
     In a case in which the user status information  1232  in the wait time indicates different values, the integration determination unit  1252  may determine that the user status information  1232  in the wait time indicates the predetermined value when a majority of the values indicated by the user status information  1232  in the wait time satisfy the predetermined value. In the example illustrated in  FIG. 8 , for example, with the data ID  182  indicating “1”, the user status indicates that the text input field is being selected for four seconds (between “2” to “5” indicated by the time  221 ) in the wait time of five seconds. Thus, the integration determination unit  1252  determines that the user status information  1232  in the wait time indicates the predetermined value. 
       FIG. 10  is a diagram illustrating an exemplary speech continuation determination result  1233  according to Embodiment 1. Any element identical to that in  FIG. 7  is denoted by an identical reference sign. 
     As illustrated in  FIG. 10 , the speech continuation determination result  1233  includes a determination result  231  indicating speech continuation determination  232  for the user sentence  185  included in the estimation result  191  illustrated in  FIG. 7 . In other words, each determination result  231  includes the system intention  183 , the system sentence  184 , the user sentence  185 , and the predicted response delay amount  192 , and the speech continuation determination  232 , to which the data ID  182  is provided. The speech continuation determination  232  indicates a result of the determination by the integration determination unit  1252 , which is the value “1” indicating the speech continuation flag or the value “0” indicating the speech discontinuation flag. 
     [Operation of Speech Continuation Determination System  1 ] 
     The following describes operation of the speech continuation determination system  1  configured as described above. 
       FIG. 11  is a flowchart illustrating the outline of operation of the speech continuation determination system  1  according to Embodiment 1. 
     First, the speech continuation determination system  1  performs, by using the learning data  1011  included in the corpus  101 , learning processing to learn the response delay amount illustrating a wait time until a response sentence is output, in other words, a wait time for a response to user speech (S 1 ). More specifically, at S 1 , the learning processing device  11  first acquires the learning data  1011  from the corpus  101  stored in the storage device  10  (S 11 ). Then, the learning processing device  11  performs learning of a predetermined model for estimating the response delay amount by using the learning data  1011  acquired at S 11  (S 12 ). Then, the learning processing device  11  stores, as the learning result information  1131 , a learning parameter such as a weight of the model as a result of the learning at S 11  (S 13 ). 
     Subsequently, the speech continuation determination system  1  determines user speech continuation by using a result of the learning processing at S 1  (S 2 ). 
     More specifically, at S 2 , the speech continuation determination device  12  first performs estimation processing and then performs determination processing. In other words, the speech continuation determination device  12  reads the learning result information  1131  as the result of the learning processing at S 1  (S 21 ), and reflects the result onto the predetermined model. Then, the speech continuation determination device  12  acquires the dialogue data  102  stored in the storage device  10  (S 22 ). Then, the speech continuation determination device  12  estimates the predicted response delay amount  192  for the user sentence  185  included in the dialogue data  102  by using the predetermined model on which the learning result information  1131  is reflected (S 23 ). Then, the speech continuation determination device  12  acquires the user status information  1232  indicating the user status (S 24 ), and determines whether the user speech continues in accordance with the user status information  1232  in the wait time indicated by the predicted response delay amount  192  (S 25 ). 
     &lt;Operation of Learning Processing Device  11 &gt; 
     The following describes in detail the learning processing (S 1 ) performed by the learning processing device  11 . 
       FIG. 12  is a detailed flowchart of the learning processing according to Embodiment 1. Any element identical to that in  FIG. 11  is denoted by an identical reference sign. The following description is made with the example illustrated in  FIG. 3  or any other drawing. 
     First, the learning processing device  11  initializes all weight parameters as learning targets (S 10 ). Specifically, the learning processing device  11  initializes the first weight parameter and the second weight parameter (learning parameters) of the first model and the second model included in the predetermined model based on a pseudo random number table. 
     Subsequently, the learning processing device  11  acquires the learning data  1011  (S 11 ). Specifically, the learning processing device  11  acquires one of a plurality of pieces of the learning data  1011  included in the corpus  101  stored in the storage device  10 . 
     Subsequently, the learning processing device  11  performs learning by using the learning data  1011  acquired at S 11  (S 12 ). Specifically, the learning processing device  11  uses, as teacher data, the response delay amount  146  included in the learning data  1011  acquired at S 11 . The learning processing device  11  causes the predetermined model to learn the correspondence between the user sentence  145  as a learning processing target and the response delay amount  146  by using the teacher data. 
     More specifically, as illustrated in  FIG. 12 , at S 12 , the learning processing device  11  first converts each user sentence  145  acquired at S 11  into a word string through morpheme analysis (S 121 ). 
     Then, the learning processing device  11  converts the word string obtained at S 121  and a dialogue action and a keyword included in the system intention  143  into a feature vector in which the word string, the dialogue action, and the keyword included are expressed as features (S 122 ). 
     Then, the learning processing device  11  calculates, by using the predetermined model, the first characteristic vector indicating a dialogue state from the feature vector obtained through the conversion at S 122  (S 123 ). Specifically, the learning processing device  11  calculates, by using the first model included in the predetermined model, the first characteristic vector indicating a dialogue state based on the feature vector obtained through the conversion at S 122  and the first weight parameter. 
     Then, the learning processing device  11  estimates, by using the predetermined model, the response delay amount for the user sentence  145  from the first characteristic vector calculated at S 123  (S 124 ). Specifically, the learning processing device  11  calculates, by using the second model included in the predetermined model, the posterior probability indicating the probability distribution of the response delay amount (zero or one) for the user sentence  145  based on the first characteristic vector calculated at S 123  and the second weight parameter. 
     Then, the learning processing device  11  update the learning parameters by using the response delay amount estimated at S 124  and the response delay amount  146  included in the learning data  1011  as the teacher data (S 125 ). Specifically, the learning processing device  11  updates, based on the posterior probability of the response delay amount for the user sentence  145 , which is calculated at S 123 , and the response delay amount that is a true value (teacher data) indicated by the response delay amount  146  included in the learning data  1011 , the first and second weight parameters so that the prediction error between the posterior probability and the true value is minimized. 
     Then, the learning processing device  11  determines whether to end the learning processing at S 12  (S 126 ). When the learning processing at S 12  is not to be ended (No at S 126 ), the learning processing device  11  returns to S 11  to acquire the learning data  1011  with the next data ID  142  or any other learning data  1011  and then performs the learning processing at S 12 . Accordingly, the learning processing device  11  repeatedly executes the learning processing while changing the learning data  1011  as a learning processing target, until learning converges. 
     When the learning processing is to be ended (Yes at S 126 ), the learning processing device  11  stores, in the storage unit  113 , the learning result information  1131  indicating a result of the learning processing (S 13 ). Specifically, the learning processing device  11  stores the first and second weight parameters (learning parameters) when the learning processing ends, as the learning result information  1131  in the storage unit  113 . When the error is not further reduced through the learning repetition, the learning processing device  11  determines that the prediction error from the true value is minimized, and ends the learning processing. 
     The following describes an example in which the learning processing device  11  performs the learning processing with the learning data  1011  illustrated in  FIG. 3 . 
     The learning processing device  11  performs the learning processing with the training data  141  in which the data ID  142  indicates “1”, the system intention  143  indicates “YES/NO question &lt;Kaisendon&gt;”, and the user sentence  145  indicates “I had fish food yesterday, (kinousakanawotabetanode,)”. In this case, the learning processing device  11  calculates, by using the first model, the first characteristic vector on which the words “node” and “,” included in the user sentence  145  are reflected as important characteristics, and learns, by using the second model, the first and second weight parameters, which associate the important characteristics and the response delay amount for the user sentence  145 , from the first characteristic vector so that the response delay amount is estimated to be one. Then, the learning processing device  11  sequentially performs such learning processing at each increment of the value of the data ID  142 . 
     For example, the learning processing device  11  performs the learning processing with the training data  141  in which the data ID  142  indicates “4”, the system intention  143  indicates “open question”, and the user sentence  145  indicates “Hello”. In this case, the learning processing device  11  calculates, by using the first model, the first characteristic vector on which “open question” and “Hello” are reflected as important characteristics, and learns, by using the second model, the first and second weight parameters, which associate the important characteristics and the response delay amount, from the first characteristic vector so that the response delay amount for the user sentence  145  is estimated to be one. 
     The same procedure is applied when the learning processing device  11  performs the learning processing with the learning data  1011  including, as the training data  141 , the system intention  183  and the user sentence  185  as illustrated in  FIG. 7 . Specifically, the learning processing device  11  performs the learning processing with the training data  141  in which the data ID  182  indicates “1”, the system intention  183  indicates “YES/NO question &lt;Chinese food&gt;”, and the user sentence  185  indicates “I&#39;ve just had Chinese food, (chuukaryouriwotabetabakarinanode,)”. In this case, the learning processing device  11  may calculate, by using the first model, the first characteristic vector on which the words “nanode” and “,” included in the user sentence  145  are reflected as important characteristics, and learn, by using the second model, the first and second weight parameters, which associate the important characteristics and the response delay amount, from the first characteristic vector so that the response delay amount for the user sentence  145  is estimated to be one. Then, the learning processing device  11  may sequentially perform such learning processing at each increment of the value of the data ID  182 . 
     &lt;Operation of Speech Continuation Determination Device  12 &gt; 
     The following describes in detail speech continuation determination performed by the speech continuation determination device  12 . 
       FIG. 13  is a flowchart illustrating details of the estimation processing illustrated in  FIG. 11 . Any element identical to that in  FIG. 11  is denoted by an identical reference sign. 
     First at S 21 , the speech continuation determination device  12  reads the learning result information  1131  as the result of the learning processing at S 1 , and reflects the learning result information  1131  onto the predetermined model. More specifically, the speech continuation determination device  12  reads the learning result information  1131 , and reflects the learning result information  1131  onto the first weight parameter of the first model and the second weight parameter of the second model. 
     Subsequently at S 22 , the speech continuation determination device  12  acquires the dialogue data  102  stored in the storage device  10 . Specifically, in the example illustrated in  FIG. 5 , the speech continuation determination device  12  acquires the dialogue data  102  including the system sentence  184  at the first time, the user sentence  185  spoken by the user at the second time following the system sentence  184 , and structured data that is data obtained by structuring intention indicated by the system sentence  184 . 
     Subsequently, the speech continuation determination device  12  converts each dialogue data  102  acquired at S 21  into a word string through morpheme analysis (S 231 ). Specifically, the speech continuation determination device  12  converts each user sentence  185  included in the speech data  181  acquired at S 21  into a word string by dividing the user sentence  185  into words through morpheme analysis. 
     Subsequently, the speech continuation determination device  12  calculates a feature vector based on the word string obtained at S 231  (S 232 ). Specifically, the speech continuation determination device  12  converts the word string obtained at S 121 , and a dialogue action and a keyword included in the system intention  183  of the speech data  181  acquired at S 21  into a feature vector in which the word string, the dialogue action, and the keyword are expressed as features. 
     Subsequently, the speech continuation determination device  12  calculates, by using the predetermined model subjected to learning, the first characteristic vector indicating a dialogue state from the feature vector calculated at S 232  (S 233 ). Specifically, the speech continuation determination device  12  calculates, by using the first model subjected to learning, the first characteristic vector indicating a dialogue state based on the feature vector calculated at S 232  and the first weight parameter subjected to learning. 
     Subsequently, the speech continuation determination device  12  estimates, by using the predetermined model subjected to learning, the predicted response delay amount based on the first characteristic vector calculated at S 233  (S 234 ). Specifically, the speech continuation determination device  12  calculates, by using the second model subjected to learning, the posterior probability indicating the probability distribution of the response delay amount for the user sentence  185  based on the first characteristic vector calculated at S 233  and the second weight parameter subjected to learning. Then, the speech continuation determination device  12  estimates the predicted response delay amount for the user sentence  185  by determining the predicted response delay amount  192  based on the posterior probability of the response delay amount, which is obtained at S 234 . The speech continuation determination device  12  stores the predicted response delay amount  192  as the response delay estimation result  1231  in the storage unit  123 . 
     In this manner, the speech continuation determination device  12  estimates the predicted response delay amount  192  for the user sentence  185  by applying the dialogue data  102  to the predetermined model based on the learning result information  1131 . 
     The following describes the determination processing performed by the speech continuation determination device  12 . 
       FIG. 14  is a flowchart illustrating details of the determination processing illustrated in  FIG. 11 .  FIG. 14  illustrates details of S 25  illustrated in  FIG. 11 , in particular. 
     First, the speech continuation determination device  12  reads the response delay estimation result  1231  stored in the storage unit  123 , and acquires the wait time indicated by the response delay estimation result  1231  (S 251 ). 
     Subsequently, the speech continuation determination device  12  determines whether there is the wait time acquired at S 251 , in other words, whether the wait time is larger than zero (S 252 ). 
     When the wait time is larger than zero at S 252  (Yes at S 252 ), the speech continuation determination device  12  acquires the user status information  1232  in the wait time, which is stored in the storage unit  123  (S 253 ). Specifically, when the wait time is larger than zero, the speech continuation determination device  12  first issues a transition command for transition to the waiting state to the system that outputs a system sentence. The transition command includes a command instructing the system not to output the system sentence  184  following the user sentence  185  until a predetermined time, such as five seconds, associated with the wait time or the wait time elapses. Then, the speech continuation determination device  12  acquires the user status information  1232  in the wait time, which is stored in the storage unit  123 . The speech continuation determination device  12  sequentially acquires the user status information  1232  and stores the user status information  1232  in the storage unit  123  in a manner similar to S 24  illustrated in  FIG. 11 , but may acquire the user status information  1232  and store the user status information  1232  in the storage unit  123  at the determination at S 252  or the start of the wait time. 
     Subsequently, the speech continuation determination device  12  determines whether the user status information  1232  in the wait time, which is acquired at S 253  indicates a predetermined value (S 254 ). 
     When the user status information  1232  in the wait time indicates the predetermined value at S 254  (Yes at S 254 ), the speech continuation determination device  12  determines that the user speech continues (second determination) and outputs the speech continuation flag (S 255 ). When the user status information  1232  in the wait time does not indicate the predetermined value at S 254  (No at S 254 ), the speech continuation determination device  12  determines that the user speech does not continue (first determination), and outputs the speech discontinuation flag (S 256 ). 
     When the wait time is zero at S 252  (No at S 252 ), the speech continuation determination device  12  determines that the user speech does not continue (first determination), and outputs the speech discontinuation flag (S 256 ). 
     As described above, the speech continuation determination device  12  performs the outputting by storing the speech continuation flag or the speech discontinuation flag as the speech continuation determination result  1233  in the storage unit  123 . 
     The following describes an aspect of the speech continuation determination processing with reference to, for example,  FIGS. 15A to 15C . 
       FIGS. 15A and 15B  is a diagram for description of an aspect of the first determination according to Embodiment 1.  FIG. 15C  is a diagram for description of an aspect of the second determination according to Embodiment 1. 
     In the example illustrated in  FIG. 15A , the system (in  FIGS. 15A, 15B, and 15C , S:) first outputs, to the user, the system sentence  184  of “Do you want to eat Chinese food?”, the system intention  143  of which indicates “YES/NO question &lt;Chinese food&gt;”, and then the user (in  FIGS. 15A, 15B, and 15C , U:) responds the user sentence  145  of “No”. In the example illustrated in  FIG. 15A , the user responds one speech sentence by inputting “No” to the text input field and pressing &lt;enter&gt;. In this case, the speech continuation determination device  12  performs the first determination by estimating the predicted response delay amount  192  indicating that there is no wait time (wait time=0) for the user sentence  145 , and outputs the speech discontinuation flag to the system. Then, in response to the speech discontinuation flag, the system outputs the system sentence  184  of “What kind of food do you prefer?”, the system intention  143  of which is “check question”, to the user following the user sentence  145  of “No” (in other words, with no wait time). 
     In the example illustrated in  FIG. 15B , the system first outputs the system sentence  184  of “Do you want to eat Chinese food?”, which is same as that in  FIG. 15A , to the user, and then the user responds the user sentence  145  of “I don&#39;t like it”. In the example illustrated in  FIG. 15B , too, the user responds one speech sentence by inputting “I don&#39;t like it” to the text input field and pressing &lt;enter&gt;. In this case, the speech continuation determination device  12  estimates the predicted response delay amount indicating that there is a wait time (wait time &gt;0) for the user sentence  145 , but performs the first determination because the user status information  1232  acquired in the wait time does not indicate a predetermined value, and then outputs the speech discontinuation flag to the system. Specifically, in the example illustrated in  FIG. 15B , the user status information  1232  acquired in the wait time indicates a value other than a predetermined value corresponding to no new input operation by the user. Then, in response to the speech discontinuation flag, the system outputs the system sentence  184  of “What kind of food do you prefer?”, the system intention  143  of which indicates “check question”, to the user after the wait time for the user sentence  145  of “I don&#39;t like it” elapses. The user responds the user sentence  145  of “I prefer meat” in response to the system sentence  184  of “What kind of food do you prefer?”. 
     In the example illustrated in  FIG. 15C , the system first outputs the system sentence  184  of “Do you want to eat Chinese food?”, which is same as that in  FIG. 15A , to the user, and then the user responds the user sentence  145  of “I don&#39;t like it”, which is same as that in  FIG. 15B . In this case, the speech continuation determination device  12  estimates the predicted response delay amount indicating that there is a wait time (wait time &gt;0) for the user sentence  145 , and performs the second determination because the user status information  1232  acquired in the wait time indicates a predetermined value, and then outputs the speech continuation flag to the system. Specifically, in the example illustrated in  FIG. 15C , the user status information  1232  acquired in the wait time indicates a predetermined value corresponding to input operation by the user. Then, in response to the speech continuation flag, the system outputs the system sentence  184  of “OK.”, the system intention  143  of which indicates “connection to user speech”, to the user after the wait time for the user sentence  145  of “I don&#39;t like it” elapses. In the time of connection such as the system sentence  184  of “OK.”, the user completes the input operation and responds the user sentence  145  of “I want to have meat dish”. 
     Effects and Others 
     As described above, according to the present embodiment, learning data including system intention of a past system speech sentence and a user speech sentence in response to the past system speech sentence is used to perform learning processing of learning the correspondence between the user speech sentence included in the learning data and a response delay amount for the user speech sentence by using the response delay amount as teacher data. 
     Accordingly, the response delay amount is accurately estimated from the user speech sentence following the system speech sentence. 
     According to the present embodiment, the response delay amount for the user speech sentence is estimated by using a result of the learning processing, and whether the user speech continues is determined by using a user status in a wait time indicated by the estimated response delay amount. In this manner, for example, according to the speech continuation determination method or the speech continuation determination device according to the present embodiment, the accuracy of the user speech continuation determination is improved by performing the user speech continuation determination at two stages. 
     Accordingly, the system can avoid, for example, miss of the content of user speech, which leads to smoother interaction between the system and the user. 
     Embodiment 2 
     The following describes Embodiment 2 of the present disclosure, which is usage of the above-described speech continuation determination device  12 . 
       FIG. 16  is a block diagram illustrating an exemplary configuration of a dialogue system according to Embodiment 2. The dialogue system has a voice recognition function and includes a portable terminal  40  such as a smartphone, and a server  50  on a cloud. The user can have dialogue with the system in natural language by voice through the portable terminal  40 . The portable terminal  40  and the server  50  are connected with each other through a public communication network such as the Internet. 
     &lt;Portable Terminal  40 &gt; 
     The portable terminal  40  is, for example, a smartphone or a tablet. The portable terminal  40  includes a character input device  401 , a user status acquisition device  402 , a signal processing unit  403 , an input/output control unit  404 , a communication unit  405 , and a response execution unit  406 . 
     The character input device  401  is used to input characters. The character input device  401  converts a result of inputting through, for example, a keyboard into a string, and outputs the string to the input/output control unit  404 . The character input device  401  is merely exemplary, and may include a microphone and convert user speech input by sound into characters. 
     The user status acquisition device  402  is used to acquire sensor information indicating the status of the user. The user status acquisition device  402  measures the operation state of, for example, a keyboard or a mouse, and outputs sensor information including a result of the measurement to the signal processing unit  403 . The user status acquisition device  402  may further include a voice input unit such as a microphone for inputting user speech by sound and receive a voice signal from the voice input unit. The user status acquisition device  402  may further include a sight line detection unit such as a camera for detecting the sight line of the user and sense whether the position of the sight line of the user at the sight line detection unit is on a display unit of the portable terminal  40 . The user status acquisition device  402  may further include a posture detection unit such as a camera for detecting the posture of the user and sense whether face and body directions of the user at the posture detection unit point to the display unit of the portable terminal  40 . In these cases, the display unit corresponds to the presentation unit described in Embodiment 1 and presents (displays) the system sentence  184  (first sentence) output from the server  50 . 
     The signal processing unit  403  converts the sensor information acquired by the user status acquisition device  402  into flag information indicating whether the user is operating the keyboard or the mouse, and outputs the flag information to the input/output control unit  404 . 
     The input/output control unit  404  outputs, to the communication unit  405 , a signal representing a string input through the character input device  401  and a signal representing the flag information input by the signal processing unit  403 . The input/output control unit  404  outputs, to the response execution unit  406 , a signal received from the communication unit  405 . 
     The communication unit  405  converts a signal input from the input/output control unit  404  into a communication signal having a format that enables communication, and transmits the communication signal thus obtained to the server  50  (communication unit  405 ). The communication unit  405  outputs the communication signal received from the server  50  to the input/output control unit  404 . 
     The response execution unit  406  displays, on a monitor, a response sentence indicated by the communication signal received from the server  50  by the input/output control unit  404 . The response execution unit  406  causes a display unit such as a display to display that the response execution unit  406  is “waiting” in a wait time indicated by a response delay amount indicated by the signal received from the server  50  by the input/output control unit  404 . 
     &lt;Server  50 &gt; 
     The server  50  includes a communication unit  501 , a dialogue history storage unit  502 , a language understanding unit  503 , a speech continuation determination device  504 , a dialogue strategy unit  505 , a response generation unit  506 , and a speech continuation notification device  507 . 
     The communication unit  501  receives a communication signal from the portable terminal  40 . The communication unit  501  extracts signals representing a string and flag information from the received communication signal, and outputs the extracted signals to the dialogue history storage unit  502 . The communication unit  501  converts the signals representing string and flag information and received from the speech continuation notification device  507  and the response generation unit  506  into communication signals having a format that enables communication, and outputs the communication signals thus obtained to the portable terminal  40  (communication unit  405 ). 
     The dialogue history storage unit  502  generates text data of user speech by analyzing the extracted signal representing a string, and records the text data in association with the data ID and the current time. The dialogue history storage unit  502  generates user status information indicating whether the user is inputting text by analyzing the extracted signal representing flag information, and records the generated user status information in association with the data ID and the current time. The dialogue history storage unit  502  records, in association with the data ID and the current time, a response sentence received from the response generation unit  506  by the communication unit  501  and the intention of the response sentence. 
     The language understanding unit  503  estimates a dialogue action and a keyword that indicate the intention of a user speech sentence by using the text data generated by the dialogue history storage unit  502 , and outputs a result of the estimation. 
     The speech continuation determination device  504  is, for example, the speech continuation determination device  12  illustrated in  FIG. 1 . The above-described learning processing is already completed for the speech continuation determination device  504 . The speech continuation determination device  504  generates the above-described dialogue data  102  by using the text data generated by the dialogue history storage unit  502 , performs predicted response delay amount estimation and speech continuation determination by using the dialogue data  102 , and outputs the speech continuation determination result  1233  to the dialogue strategy unit  505 . The speech continuation determination device  504  outputs generation of a wait time and a speech continuation flag to the speech continuation notification device  507 . 
     Only when a speech discontinuation flag is output from the speech continuation determination device  504 , the dialogue strategy unit  505  holds results of the dialogue action and keyword estimation by the language understanding unit  503  in a temporally sequential order, and outputs, based on the order, a dialogue action and a keyword to be responded from the system. 
     The response generation unit  506  generates a response sentence corresponding to the dialogue action received from the dialogue strategy unit  505 . The communication unit  501  converts the generated response sentence into a communication signal having a format that enables communication, and transmits the communication signal thus obtained to the portable terminal  40 . 
     When having received the generation of a wait time and a speech continuation flag, the speech continuation notification device  507  notifies, in the wait time, the communication unit  501  that the system is waiting for inputting from the user, or outputs, when the speech continuation flag is generated, a response sentence prompting the user for speech to the communication unit  501 . 
     In this manner, in the dialogue system as illustrated in  FIG. 16 , the server  50  appropriately determines whether user speech continues by using the speech continuation determination device  12  subjected to the learning processing described in Embodiment 1. Accordingly, the dialogue system can prevent, for example, the user speech from being missed, which leads to smooth interaction. 
     Although the speech continuation determination device according to the embodiments is described above, the present disclosure is not limited to the embodiments. 
     For example, each processing unit included in the speech continuation determination device according to the above-described embodiments is typically achieved as an LSI, which is an integrated circuit. The processing units may be achieved by individual chips, or may be partially or entirely achieved by one chip. 
     Integration is not limited to an LSI, but may be achieved by a dedicated circuit or a general-purpose processor. Alternatively, integration may be achieved by a field programmable gate array (FPGA) that can be programmed after LSI manufacturing, or a reconfigurable processor in which connection and setting of circuit cells inside an LSI can be reconstructed. 
     The present disclosure may be achieved as a speech continuation determination method to be executed by a speech continuation determination device. 
     In each above-described embodiment, each component may be achieved by dedicated hardware or by executing a software program suitable for the component. Each component may be achieved by a program execution unit, such as a CPU or a processor, reading and executing the software program recorded in a recording medium such as a hard disk or a semiconductor memory. 
     Division of functional blocks in each block diagram is merely exemplary. A plurality of functional blocks may be achieved as one functional block, one functional block may be divided into a plurality of functional blocks, or part of a function may be moved to another functional block. Functions of a plurality of functional blocks having similar functions may be processed in parallel or in time divisional manner by single hardware or software. 
     The execution order of steps in each flowchart is exemplarily indicated to specifically describe the present disclosure, and may be different from the above-described order. Part of each step may be executed simultaneously (in parallel) with another step. 
     Although the speech continuation determination device according to one or a plurality of aspects is described above based on the embodiments, the present disclosure is not limited to the present embodiments. Various modifications thought of by the skilled person in the art and provided to the present embodiment, and any configuration established by combining components in different embodiments may be included in the range of the one or the plurality of aspects without departing from the scope of the present disclosure. 
     The present disclosure is applicable to a speech continuation determination device and enables, for example, appropriate determination of whether user speech by voice or text continues. For example, the present disclosure is applicable to a task oriented or non task oriented dialogue system device or scheme such as a call center, medical interview dialogue, or chat dialogue, which achieve user tasks.