Patent Publication Number: US-2021166685-A1

Title: Speech processing apparatus and speech processing method

Description:
FIELD 
     The present disclosure relates to a speech processing apparatus and a speech processing method. 
     BACKGROUND 
     Speech processing apparatus with a speech agent function has recently become popular. The speech agent function is a function to analyze the meaning of speech uttered by a user and execute processing in accordance with the meaning obtained by the analysis. For example, when a user utters a speech “Send an email let&#39;s meet in Shibuya tomorrow to A”, the speech processing apparatus with the speech agent function analyzes the meaning of the speech, and sends an email having a body “Let&#39;s meet in Shibuya tomorrow” to A by using a pre-registered email address of A. Examples of other types of processing executed by the speech agent function include answering a question from a user, for example, as disclosed in Patent Literature  1 . 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP 2016-192121 A 
     SUMMARY 
     Technical Problem 
     The speech uttered by a user may include a correct speech expressing a meaning intended for conveyance by the user, and an error speech not expressing the meaning intended for conveyance by the user. The error speech is, for example, a filler such as “well” and “umm”, and a soliloquy such as “what was it?”. When a user utters speech including the error speech, the user may utter the speech again from the start to provide the speech including only the correct speech to the speech agent function. However, uttering the speech again from the start is troublesome for the user. 
     Thus, the present disclosure proposes a novel and improved speech processing apparatus and method enabling acquisition of a meaning intended for conveyance by a user from speech of the user while reducing the trouble for the user. 
     Solution to Problem 
     According to the present disclosure, a speech processing apparatus is provided that includes an analysis unit configured to analyze a meaning of speech uttered by a user based on a recognition result of the speech and an analysis result of a behavior of the user while the user is uttering the speech. 
     Moreover, according to the present disclosure, a speech processing method is provided that includes analyzing, by a processor, a meaning of speech uttered by a user based on a recognition result of the speech and an analysis result of a behavior of the user while the user is uttering the speech. 
     Advantageous Effects of Invention 
     As described above, the present disclosure enables the acquisition of the meaning intended for conveyance by the user from the speech of the user while reducing the trouble for the user. Note that the effects described above are not necessarily limitative. With or in the place of the above effects, there may be achieved any one of the effects described in this specification or other effects that may be grasped from this specification. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is an explanatory diagram illustrating an overview of a speech processing apparatus  20  according to an embodiment of the present disclosure. 
         FIG. 2  is an explanatory diagram illustrating a configuration of the speech processing apparatus  20  according to the embodiment of the present disclosure. 
         FIG. 3  is an explanatory diagram illustrating a first example of meaning correction. 
         FIG. 4  is an explanatory diagram illustrating a second example of the meaning correction. 
         FIG. 5  is an explanatory diagram illustrating a third example of the meaning correction. 
         FIG. 6  is an explanatory diagram illustrating a fourth example of the meaning correction. 
         FIG. 7  is a flowchart illustrating an operation of the speech processing apparatus  20  according to the embodiment of the present disclosure. 
         FIG. 8  is an explanatory diagram illustrating a hardware configuration of the speech processing apparatus  20 . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the appended drawings. In this specification and the appended drawings, structural elements having substantially the same function and structure are denoted with the same reference numerals, and repeated explanation of these structural elements is omitted. 
     Additionally, in this specification and the appended drawings, a plurality of structural elements having substantially the same function and structure are sometimes distinguished from each other using different alphabets after the same reference numerals. However, when a plurality of structural elements having substantially the same function and structure do not particularly have to be distinguished from each other, the structural elements are denoted only with the same reference numerals. 
     Moreover, the present disclosure will be described in the order of the following items.
         1. Overview of Speech processing apparatus   2. Configuration of Speech processing apparatus   3. Specific examples of Meaning correction
           3-1. First example   3-2. Second example   3-3. Third example   3-4. Fourth example   
           4. Operation of Speech processing apparatus   5. Modification   6. Hardware configuration   7. Conclusion       

     Overview of Speech Processing Apparatus 
     First, an overview of a speech processing apparatus according to an embodiment of the present disclosure will be described with reference to  FIG. 1 . 
       FIG. 1  is an explanatory diagram illustrating an overview of a speech processing apparatus  20  according to the embodiment of the present disclosure. As illustrated in  FIG. 1 , the speech processing apparatus  20  is placed in, for example, a house. The speech processing apparatus  20  has a speech agent function to analyze the meaning of speech uttered by a user of the speech processing apparatus  20 , and execute processing in accordance with the meaning obtained by the analysis. 
     For example, when the user of the speech processing apparatus  20  utters a speech “Send an email let&#39;s meet in Shibuya tomorrow to A” as illustrated in  FIG. 1 , the speech processing apparatus  20  analyzes the meaning of the speech, and understands that the task is to send an email, the destination is A, and the body of the email is “let&#39;s meet in Shibuya tomorrow”. The speech processing apparatus  20  sends an email having a body “Let&#39;s meet in Shibuya tomorrow” to a mobile terminal  30  of A via a network  12  by using a pre-registered email address of A. 
     Note that the speech processing apparatus  20 , which is illustrated as a stationary apparatus in  FIG. 1 , is not limited to the stationary apparatus. The speech processing apparatus  20  may be, for example, a portable information processing apparatus such as a smartphone, a mobile phone, a personal handy phone system (PHS), a portable music player, a portable video processing apparatus and a portable game console, or an autonomous mobile robot. Additionally, the network  12  is a wired or wireless transmission path for information to be transmitted from an apparatus connected to the network  12 . Examples of the network  12  may include a public network such as Internet, a phone network and a satellite communication network, and various local area networks (LAN) and wide area networks (WAN) including Ethernet (registered trademark). The network  12  may also include a dedicated network such as an Internet protocol-virtual private network (IP-VPN). 
     Here, the speech uttered by the user may include a correct speech expressing a meaning intended for conveyance by the user, and an error speech not expressing the meaning intended for conveyance by the user. The error speech is, for example, a filler such as “well” and “umm”, and a soliloquy such as “what was it?” A negative word such as “not” and a speech talking to another person also sometimes fall under the error speech. When the user utters speech including such an error speech, e.g., when the user utters a speech “Send an email let&#39;s meet in, umm . . . where is that? Shibuya tomorrow to A”, uttering the speech again from the start is troublesome for the user. 
     The inventors of this application have developed the embodiment of the present disclosure by focusing on the above circumstances. In accordance with the embodiment of the present disclosure, the meaning intended for conveyance by the user can be obtained from the speech of the user while reducing the trouble for the user. In the following, a configuration and an operation of the speech processing apparatus  20  according to the embodiment of the present disclosure will be sequentially described in detail. 
     Configuration of Speech Processing Apparatus 
       FIG. 2  is an explanatory diagram illustrating the configuration of the speech processing apparatus  20  according to the embodiment of the present disclosure. As illustrated in  FIG. 2 , the speech processing apparatus  20  includes an image processing unit  220 , a speech processing unit  240 , an analysis unit  260 , and a processing execution unit  280 . 
     (Image Processing Unit) 
     The image processing unit  220  includes an imaging unit  221 , a face image extraction unit  222 , an eye feature value extraction unit  223 , a visual line identification unit  224 , a face feature value extraction unit  225 , and a facial expression identification unit  226  as illustrated in  FIG. 2 . 
     The imaging unit  221  captures an image of a subject to acquire the image of the subject. The imaging unit  221  outputs the acquired image of the subject to the face image extraction unit  222 . 
     The face image extraction unit  222  determines whether a person area exists in the image input from the imaging unit  221 . When the person area exists in the imaging unit  221 , the face image extraction unit  222  extracts a face image in the person area to identify a user. The face image extracted by the face image extraction unit  222  is output to the eye feature value extraction unit  223  and the face feature value extraction unit  225 . 
     The eye feature value extraction unit  223  analyzes the face image input from the face image extraction unit  222  to extract a feature value for identifying a visual line of the user. 
     The visual line identification unit  224 , which is an example of a behavior analysis unit configured to analyze user behaviors, identifies a direction of the visual line based on the feature value extracted by the eye feature value extraction unit  223 . The visual line identification unit  224  identifies a face direction in addition to the visual line direction. The visual line direction, a change in the visual line, and the face direction obtained by the visual line identification unit  224  are output to the analysis unit  260  as an example of analysis results of the user behaviors. 
     The face feature value extraction unit  225  extracts a feature value for identifying a facial expression of the user based on the face image input from the face image extraction unit  222 . 
     The facial expression identification unit  226 , which is an example of the behavior analysis unit configured to analyze the user behaviors, identifies the facial expression of the user based on the feature value extracted by the face feature value extraction unit  225 . For example, the facial expression identification unit  226  may identify an emotion corresponding to the facial expression by recognizing whether the user changes his/her facial expression during utterance, and which emotion the change in the facial expression is based on, e.g., whether the user is angry, laughing, or embarrassed. A correspondence relation between the facial expression and the emotion may be explicitly given by a designer as a rule using a state of eyes or a mouth, or may be obtained by a method of preparing data in which the facial expression and the emotion are associated with each other and performing statistical learning using the data. Additionally, the facial expression identification unit  226  may identify the facial expression of the user by utilizing time series information based on a moving image, or by preparing a reference image (e.g., an image with a blank expression), and comparing the face image output from the face image extraction unit  222  with the reference image. The facial expression of the user and a change in the facial expression of the user identified by the facial expression identification unit  226  are output to the analysis unit  260  as an example of the analysis results of the user behaviors. Note that the speech processing apparatus  20  can also obtain whether the user is talking to another person or is uttering speech to the speech processing apparatus  20  by using the image obtained by the imaging unit  221  as the analysis results of the user behaviors. 
     (Speech Processing Unit) 
     The speech processing unit  240  includes a sound collection unit  241 , a speech section detection unit  242 , a speech recognition unit  243 , a word detection unit  244 , an utterance direction estimation unit  245 , a speech feature detection unit  246 , and an emotion identification unit  247  as illustrated in  FIG. 2 . 
     The sound collection unit  241  has a function as a speech input unit configured to acquire an electrical sound signal from air vibration containing environmental sound and speech. The sound collection unit  241  outputs the acquired sound signal to the speech section detection unit  242 . 
     The speech section detection unit  242  analyzes the sound signal input from the sound collection unit  241 , and detects a speech section equivalent to a speech signal in the sound signal by using an intensity (amplitude) of the sound signal and a feature value indicating a speech likelihood. The speech section detection unit  242  outputs the sound signal corresponding to the speech section, i.e., the speech signal to the speech recognition unit  243 , the utterance direction estimation unit  245 , and the speech feature detection unit  246 . The speech section detection unit  242  may obtain a plurality of speech sections by dividing one utterance section by a break of the speech. 
     The speech recognition unit  243  recognizes the speech signal input from the speech section detection unit  242  to obtain a character string representing the speech uttered by the user. The character string obtained by the speech recognition unit  243  is output to the word detection unit  244  and the analysis unit  260 . 
     The word detection unit  244  stores therein a list of words possibly falling under the error speech not expressing the meaning intended for conveyance by the user, and detects the stored word from the character string input from the speech recognition unit  243 . The word detection unit  244  stores therein, for example, words falling under the filler such as “well” and “umm”, words falling under the soliloquy such as “what was it?” and words corresponding to the negative word such as “not” as the words possibly falling under the error speech. The word detection unit  244  outputs the detected word and an attribute (e.g., the filler or the negative word) of this word to the analysis unit  260 . 
     The utterance direction estimation unit  245 , which is an example of the behavior analysis unit configured to analyze the user behaviors, analyzes the speech signal input from the speech section detection unit  242  to estimate a user direction as viewed from the speech processing apparatus  20 . When the sound collection unit  241  includes a plurality of sound collection elements, the utterance direction estimation unit  245  can estimate the user direction, which is a speech source direction, and movement of the user as viewed from the speech processing apparatus  20  based on a phase difference between speech signals obtained by the respective sound collection elements. The user direction and the user movement are output to the analysis unit  260  as an example of the analysis results of the user behaviors. 
     The speech feature detection unit  246  detects a speech feature such as a voice volume, a voice pitch and a pitch fluctuation from the speech signal input from the speech section detection unit  242 . Note that the speech feature detection unit  246  can also calculate an utterance speed based on the character string obtained by the speech recognition unit  243  and the length of the speech section detected by the speech section detection unit  242 . 
     The emotion identification unit  247 , which is an example of the behavior analysis unit configured to analyze the user behaviors, identifies an emotion of the user based on the speech feature detected by the speech feature detection unit  246 . For example, the emotion identification unit  247  acquires, based on the speech feature detected by the speech feature detection unit  246 , information expressed in the voice depending on the emotion, e.g., an articulation degree such as whether the user speaks clearly or unclearly, a relative utterance speed in comparison with a normal utterance speed, and whether the user is angry or embarrassed. A correspondence relation between the speech and the emotion may be explicitly given by a designer as a rule using a voice state, or may be obtained by a method of preparing data in which the voice and the emotion are associated with each other and performing statistical learning using the data. Additionally, the facial expression identification unit  226  may identify the emotion of the user by preparing a reference voice of the user, and comparing the speech output from the speech section detection unit  242  with the reference voice. The user emotion and a change in the emotion identified by the emotion identification unit  247  are output to the analysis unit  260  as an example of the analysis results of the user behaviors. 
     (Analysis Unit) 
     The analysis unit  260  includes a meaning analysis unit  262 , a storage unit  264 , and a correction unit  266  as illustrated in  FIG. 2 . 
     The meaning analysis unit  262  analyzes the meaning of the character string input from the speech recognition unit  243 . For example, when a character string “Send an email I won&#39;t need dinner tomorrow to Mom” is input, the meaning analysis unit  262  has a portion to perform morphological analysis on the character string and determine that the task is “to send an email” based on keywords such as “send” and “email”, and a portion to acquire the destination and the body as necessary arguments for achieving the task. In this example, “Mom” is acquired as the destination, and “I won&#39;t need dinner tomorrow” as the body. The meaning analysis unit  262  outputs these analysis results to the correction unit  266 . 
     Note that a meaning analysis method may be any of a method of achieving the meaning analysis by machine learning using an utterance corpus created in advance, a method of achieving the meaning analysis by a rule, or a combination thereof. Additionally, to perform the morphological analysis as a part of the meaning analysis processing, the meaning analysis unit  262  has a mechanism of giving an attribute to each word, and an internal dictionary. The meaning analysis unit  262  can provide what kind of word the word included in the uttered speech is, that is, the attribute such as a person name, a place name and a common noun in accordance with the attribute giving mechanism and the dictionary. 
     The storage unit  264  stores therein a history of information regarding the user. The storage unit  264  may store therein information indicating, for example, what kind of order the user has given to the speech processing apparatus  20  by speech, and what kind of condition the image processing unit  220  and the speech processing unit  240  have identified regarding the user. 
     The correction unit  266  corrects the analysis results of the character string obtained by the meaning analysis unit  262 . The correction unit  266  specifies a portion corresponding to the error speech included in the character string based on, for example, the change in the visual line of the user input from the visual line identification unit  224 , the change in the facial expression of the user input from the facial expression identification unit  226 , the word detection results input from the word detection unit  244 , and the history of the information regarding the user stored in the storage unit  264 , and corrects the portion corresponding to the error speech by deleting or replacing the portion. The correction unit  266  may specify the portion corresponding to the error speech in accordance with a rule in which a relation between each input and the error speech is described, or based on statistical learning of each input. The specification and correction processing of the portion corresponding to the error speech by the correction unit  266  will be more specifically described in “3. Specific examples of Meaning correction”. 
     (Processing Execution Unit) 
     The processing execution unit  280  executes processing in accordance with the meaning corrected by the correction unit  266 . The processing execution unit  280  may be, for example, a communication unit that sends an email, a schedule management unit that inputs an appointment to a schedule, an answer processing unit that answers a question from the user, an appliance control unit that controls operations of household electrical appliances, or a display control unit that changes display contents in accordance with the meaning corrected by the correction unit  266 . 
     SPECIFIC EXAMPLES OF MEANING CORRECTION 
     The configuration of the speech processing apparatus  20  according to the embodiment of the present disclosure has been described above. Subsequently, some specific examples of the meaning correction performed by the facial expression identification unit  226  of the speech processing apparatus  20  will be sequentially described. 
     First Example 
       FIG. 3  is an explanatory diagram illustrating a first example of the meaning correction.  FIG. 3  illustrates an example in which a user utters a speech “Send an email let&#39;s meet in, umm . . . where is that? Shibuya tomorrow to A”. In this example, the speech section detection unit  242  detects a speech section A 1  corresponding to a speech “tomorrow”, a speech section A 2  corresponding to a speech “umm . . . where is that?” and a speech section A 3  corresponding to a speech “send an email let&#39;s meet in Shibuya to A” from one utterance section. The meaning analysis unit  262  analyzes the speech to acquire that the task is to send an email, the destination is A, and the body of the email is “let&#39;s meet in, umm . . . where is that? Shibuya tomorrow”. 
     Moreover, in the example of  FIG. 3 , the visual line identification unit  224  identifies that the visual line direction is front in the speech sections A 1  and A 3  and left in the speech section A 2 . The facial expression identification unit  226  identifies that the facial expression is a blank expression throughout the speech sections A 1  to A 3 . The word detection unit  244  detects “umm” falling under the filler in the speech section A 2 . The utterance direction estimation unit  245  estimates that the utterance direction is front throughout the speech sections A 1  to A 3 . 
     The correction unit  266  specifies whether each speech portion uttered by the user corresponds to the correct speech or the error speech based on the analysis results of the user behaviors such as the visual line direction, the facial expression and the utterance direction, and the detection of the filler. In the example illustrated in  FIG. 3 , the correction unit  266  specifies the speech portion corresponding to the speech section A 2  as the error speech (a soliloquy or talking to another person) based on the facts that the filler is detected in the speech section A 2 , the visual line is directed to another direction in the speech section A 2 , and the speech section A 2  is determined as a portion representing the email body. 
     As a result, the correction unit  266  deletes the meaning of the portion corresponding to the speech section A 2  from the meaning of the uttered speech acquired by the meaning analysis unit  262 . That is, the correction unit  266  corrects the meaning of the email body from “let&#39;s meet in, umm . . . where is that? Shibuya tomorrow” to “let&#39;s meet in Shibuya tomorrow”. With such a configuration, the processing execution unit  280  sends an email having a body “Let&#39;s meet in Shibuya tomorrow” intended for conveyance by the user to A. 
     Second Example 
       FIG. 4  is an explanatory diagram illustrating a second example of the meaning correction.  FIG. 4  illustrates an example in which a user utters a speech “Schedule meeting in Shinjuku, not in Shibuya for tomorrow”. In this example, the speech section detection unit  242  detects a speech section B 1  corresponding to a speech “for tomorrow”, a speech section B 2  corresponding to a speech “in Shibuya”, and a speech section B 3  corresponding to a speech “schedule meeting in Shinjuku, not” from one utterance section. The meaning analysis unit  262  analyzes the speech to acquire that the task is to register a schedule, the date is tomorrow, the content is “meeting in Shinjuku, not in Shibuya”, and the word attribute of Shibuya and Shinjuku is a place name. 
     Moreover, in the example of  FIG. 4 , the visual line identification unit  224  identifies that the visual line direction is front throughout the speech sections B 1  to B 3 . The facial expression identification unit  226  detects a change in the facial expression in the speech section B 3 . The word detection unit  244  detects “not” falling under the negative word in the speech section B 2 . The utterance direction estimation unit  245  estimates that the utterance direction is front throughout the speech sections B 1  to B 3 . 
     The correction unit  266  specifies whether each speech portion uttered by the user corresponds to the correct speech or the error speech based on the analysis results of the user behaviors such as the visual line direction, the facial expression and the utterance direction, and the detection of the negative word. In the example illustrated in  FIG. 4 , the correction unit  266  determines that the user corrects the place name during the utterance and specifies the speech portion corresponding to “not in Shibuya” as the error speech based on the facts that the negative word is detected in the speech section B 3 , the place names are placed before and after the negative word “not”, and the change in the facial expression is detected during the utterance of the negative word “not”. 
     As a result, the correction unit  266  deletes the meaning of the speech portion corresponding to “not in Shibuya” from the meaning of the uttered speech acquired by the meaning analysis unit  262 . That is, the correction unit  266  corrects the content of the schedule from “meeting in Shinjuku, not in Shibuya” to “meeting in Shinjuku”. With such a configuration, the processing execution unit  280  registers “meeting in Shinjuku” as a schedule for tomorrow. 
     Third Example 
       FIG. 5  is an explanatory diagram illustrating a third example of the meaning correction.  FIG. 5  illustrates an example in which a user utters a speech “Send an email let&#39;s meet in Shinjuku, not in Shibuya to B”. In this example, the speech section detection unit  242  detects a speech section C 1  corresponding to a speech “to B”, a speech section C 2  corresponding to a speech “let&#39;s meet in Shinjuku, not in Shibuya”, and a speech section C 3  corresponding to a speech “send an email” from one utterance section. The meaning analysis unit  262  analyzes the speech to acquire that the task is to send an email, the destination is B, the body is “let&#39;s meet in Shinjuku, not in Shibuya”, and the word attribute of Shibuya and Shinjuku is a place name. 
     Moreover, in the example of  FIG. 5 , the visual line identification unit  224  identifies that the visual line direction is front throughout the speech sections C 1  to C 3 . The facial expression identification unit  226  detects that the facial expression is a blank expression throughout the speech sections C 1  to C 3 . The word detection unit  244  detects “not” falling under the negative word in the speech section C 2 . The utterance direction estimation unit  245  estimates that the utterance direction is front throughout the speech sections C 1  to C 3 . 
     The correction unit  266  specifies whether each speech portion uttered by the user corresponds to the correct speech or the error speech based on the analysis results of the user behaviors such as the visual line direction, the facial expression and the utterance direction, and the detection of the negative word. In the example illustrated in  FIG. 5 , the negative word “not” is detected in the speech section C 2 . However, no change is detected in the user behaviors such as the visual line, the facial expression and the utterance direction. Moreover, the storage unit  264  stores therein information indicating that a relation between B and the user is “friends”. The body of the email between friends may include the negative word in spoken language. The email body can also include the negative word. Based on the situation and circumstances, the correction unit  266  does not treat the negative word “not” in the speech section C 2  as the error speech. That is, the correction unit  266  does not correct the meaning of the uttered speech acquired by the meaning analysis unit  262 . As a result, the processing execution unit  280  sends an email having a body “Let&#39;s meet in Shinjuku, not in Shibuya” to B. 
     Fourth Example 
       FIG. 6  is an explanatory diagram illustrating a fourth example of the meaning correction.  FIG. 6  illustrates an example in which a user  1  utters a speech “Send an email let&#39;s meet in, umm . . . where is that”, a user  2  utters a speech “Shibuya”, and the user  1  utters a speech “Shibuya tomorrow to C”. In this example, the speech section detection unit  242  detects a speech section D 1  corresponding to a speech “tomorrow”, a speech section D 2  corresponding to a speech “umm . . . where is that?” a speech section D 3  corresponding to a speech “Shibuya”, and a speech section D 4  corresponding to a speech “send an email let&#39;s meet in Shibuya to C” from one utterance section. The meaning analysis unit  262  analyzes the speech to acquire that the task is to send an email, the destination is C, and the body is “let&#39;s meet in, umm . . . where is that? Shibuya. Shibuya tomorrow”. 
     Moreover, in the example of  FIG. 6 , the visual line identification unit  224  identifies that the visual line direction is front in the speech sections D 1  and D 4  and left throughout the speech sections D 2  to D 3 . The facial expression identification unit  226  detects that the facial expression is a blank expression throughout the speech sections D 1  to D 4 . The word detection unit  244  detects “umm” falling under the filler in the speech section D 2 . The utterance direction estimation unit  245  estimates that the utterance direction is front in the speech sections D 1  to D 2  and D 4 , and left in the speech section D 3 . 
     The correction unit  266  specifies whether each speech portion uttered by the user corresponds to the correct speech or the error speech based on the analysis results of the user behaviors such as the visual line direction, the facial expression and the utterance direction, and the detection of the filler. In the example illustrated in  FIG. 6 , the correction unit  266  specifies the speech portion corresponding to the speech section D 2  as the error speech (a soliloquy or talking to another person) based on the facts that the filler “umm” is detected in the speech section D 2 , the visual line is changed to left in the speech section D 2 , and the speech section D 2  is determined as a portion representing the email body. 
     Additionally, in the example illustrated in  FIG. 6 , the utterance direction is changed to left in the speech section D 3 . Thus, the speech in the speech section D 3  is considered to be uttered by a different user from the user who has uttered the speech in the other speech sections. Consequently, the correction unit  266  specifies the speech portion corresponding to the speech section D 3  as the error speech (uttered by another person). 
     As a result, the correction unit  266  deletes the meanings of the portions corresponding to the speech sections D 2  and D 3  from the meaning of the uttered speech acquired by the meaning analysis unit  262 . That is, the correction unit  266  corrects the meaning of the email body from “let&#39;s meet in, umm . . . where is that? Shibuya. Shibuya tomorrow” to “let&#39;s meet in Shibuya tomorrow”. With such a configuration, the processing execution unit  280  sends an email having a body “Let&#39;s meet in Shibuya tomorrow” intended for conveyance by the user to C. 
     The example in which the speech uttered by a user other than the user who has uttered the speech to be processed by the speech processing apparatus  20  is also input to the meaning analysis unit  262  has been described above. Alternatively, the speech acquired to be uttered by another user based on the utterance direction estimated by the utterance direction estimation unit  245  may be deleted before input to the meaning analysis unit  262 . 
     Operation of Speech Processing Apparatus 
     The configuration of the speech processing apparatus  20  and the specific examples of the processing according to the embodiment of the present disclosure have been described above. Subsequently, the operation of the speech processing apparatus  20  according to the embodiment of the present disclosure will be described with reference to  FIG. 7 . 
       FIG. 7  is a flowchart illustrating the operation of the speech processing apparatus  20  according to the embodiment of the present disclosure. As illustrated in  FIG. 7 , the speech section detection unit  242  of the speech processing apparatus  20  according to the embodiment of the present disclosure analyzes the sound signal input from the sound collection unit  241 , and detects the speech section equivalent to the speech signal in the sound signal by using the intensity (amplitude) of the sound signal and the feature value indicating a speech likelihood (S 310 ). 
     The speech recognition unit  243  recognizes the speech signal input from the speech section detection unit  242  to obtain the character string representing the speech uttered by the user (S 320 ). The meaning analysis unit  262  then analyzes the meaning of the character string input from the speech recognition unit  243  (S 330 ). 
     In parallel with the above steps at  5310  to  5330 , the speech processing apparatus  20  analyzes the user behaviors (S 340 ). For example, the visual line identification unit  224  of the speech processing apparatus  20  identifies the visual line direction of the user, and the facial expression identification unit  226  identifies the facial expression of the user. 
     After that, the correction unit  266  corrects the analysis results of the character string obtained by the meaning analysis unit  262  based on the history information stored in the storage unit  264  and the analysis results of the user behaviors (S 350 ). The processing execution unit  280  executes the processing in accordance with the meaning corrected by the correction unit  266  (S 360 ). 
     Modification 
     The embodiment of the present disclosure has been described above. Hereinafter, some modifications of the embodiment of the present disclosure will be described. Note that the respective modifications described below may be applied to the embodiment of the present disclosure individually or by combination. Additionally, the respective modifications may be applied instead of the configuration described in the embodiment of the present disclosure or added to the configuration described in the embodiment of the present disclosure. 
     For example, the function of the correction unit  266  may be enabled/disabled depending on an application to be used, that is, the task in accordance with the meaning analyzed by the meaning analysis unit  262 . To be more specific, the error speech may be easily generated in some applications, and difficult to be generated in other applications. In this case, the function of the correction unit  266  is disabled in the application in which the error speech is difficult to be generated and is enabled in the application in which the error speech is easily generated. This allows prevention of correction not intended by the user. 
     Additionally, the above embodiment has described the example in which the correction unit  266  performs the meaning correction after the meaning analysis performed by the meaning analysis unit  262 . The processing order and the processing contents are not limited to the above example. For example, the correction unit  266  may delete the error speech portion first, and the meaning analysis unit  262  may then analyze the meaning of the character string from which the error speech portion has been deleted. This configuration can shorten the length of the character string as a target of the meaning analysis performed by the meaning analysis unit  262 , and reduce the processing load on the meaning analysis unit  262 . 
     Moreover, the above embodiment has described the example in which the speech processing apparatus  20  has the plurality of functions illustrated in  FIG. 2  implemented therein. Alternatively, the functions illustrated in  FIG. 2  may be at least partially implemented in an external server. For example, the functions of the eye feature value extraction unit  223 , the visual line identification unit  224 , the face feature value extraction unit  225 , the facial expression identification unit  226 , the speech section detection unit  242 , the speech recognition unit  243 , the utterance direction estimation unit  245 , the speech feature detection unit  246 , and the emotion identification unit  247  may be implemented in a cloud server on the network. The function of the word detection unit  244  may be implemented not only in the speech processing apparatus  20  but also in the cloud server on the network. The analysis unit  260  may be also implemented in the cloud server. In this case, the cloud server functions as the speech processing apparatus. 
     Hardware Configuration 
     The embodiment of the present disclosure has been described above. The information processing such as the image processing, the speech processing and the meaning analysis described above is achieved by cooperation between software and hardware of the speech processing apparatus  20  described below. 
       FIG. 8  is an explanatory diagram illustrating a hardware configuration of the speech processing apparatus  20 . As illustrated in  FIG. 8 , the speech processing apparatus  20  includes a central processing unit (CPU)  201 , a read only memory (ROM)  202 , a random access memory (RAM)  203 , an input device  208 , an output device  210 , a storage device  211 , a drive  212 , an imaging device  213 , and a communication device  215 . 
     The CPU  201  functions as an arithmetic processor and a controller and controls the entire operation of the speech processing apparatus  20  in accordance with various computer programs. The CPU  201  may be also a microprocessor. The ROM  202  stores computer programs, operation parameters or the like to be used by the CPU  201 . The RAM  203  temporarily stores computer programs to be used in execution of the CPU  201 , parameters that appropriately change in the execution, or the like. These units are connected mutually via a host bus including, for example, a CPU bus. The CPU  201 , the ROM  202 , and the RAM  203  can cooperate with software to achieve the functions of, for example, the eye feature value extraction unit  223 , the visual line identification unit  224 , the face feature value extraction unit  225 , the facial expression identification unit  226 , the speech section detection unit  242 , the speech recognition unit  243 , the word detection unit  244 , the utterance direction estimation unit  245 , the speech feature detection unit  246 , the emotion identification unit  247 , the analysis unit  260 , and the processing execution unit  280  described with reference to  FIG. 2 . 
     The input device  208  includes an input unit that allows the user to input information, such as a mouse, a keyboard, a touch panel, a button, a microphone, a switch and a lever, and an input control circuit that generates an input signal based on the input from the user and outputs the input signal to the CPU  201 . The user of the speech processing apparatus  20  can input various data or instruct processing operations to the speech processing apparatus  20  by operating the input device  208 . 
     The output device  210  includes a display device such as a liquid crystal display (LCD) device, an organic light emitting diode (OLED) device, and a lamp. The output device  210  further includes a speech output device such as a speaker and a headphone. The display device displays, for example, a captured image or a generated image. Meanwhile, the speech output device converts speech data or the like to a speech and outputs the speech. 
     The storage device  211  is a data storage device configured as an example of the storage unit of the speech processing apparatus  20  according to the present embodiment. The storage device  211  may include a storage medium, a recording device that records data on the storage medium, a read-out device that reads out the data from the storage medium, and a deleting device that deletes the data recorded on the storage medium. The storage device  211  stores therein computer programs to be executed by the CPU  201  and various data. 
     The drive  212  is a storage medium reader-writer, and is incorporated in or externally connected to the speech processing apparatus  20 . The drive  212  reads out information recorded on a removable storage medium  24  such as a magnetic disk, an optical disk, a magneto-optical disk or a semiconductor memory loaded thereinto, and outputs the information to the RAM  203 . The drive  212  can also write information onto the removable storage medium  24 . 
     The imaging device  213  includes an imaging optical system such as a photographic lens and a zoom lens for collecting light, and a signal conversion element such as a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS). The imaging optical system collects light emitted from a subject to form a subject image on the signal conversion unit, and the signal conversion element converts the formed subject image to an electrical image signal. 
     The communication device  215  is, for example, a communication interface including a communication device to be connected to the network  12 . The communication device  215  may be also a wireless local area network (LAN) compatible communication device, a long term evolution (LTE) compatible communication device, or a wired communication device that performs wired communication. 
     Conclusion 
     In accordance with the embodiment of the present disclosure described above, various effects can be obtained. 
     For example, the speech processing apparatus  20  according to the embodiment of the present disclosure specifies the portion corresponding to the correct speech and the portion corresponding to the error speech by using not only the detection of a particular word but also the user behaviors when the particular word is detected. Consequently, a more appropriate specification result can be obtained. The speech processing apparatus  20  according to the embodiment of the present disclosure can also specify the speech uttered by a different user from the user who has uttered the speech to the speech processing apparatus  20  as the error speech by further using the utterance direction. 
     The speech processing apparatus  20  according to the embodiment of the present disclosure deletes or corrects the meaning of the portion specified as the error speech. Thus, even when the speech of the user includes the error speech, the speech processing apparatus  20  can obtain the meaning intended for conveyance by the user from the speech of the user without requiring the user to utter the speech again. As a result, the trouble for the user can be reduced. 
     The preferred embodiment(s) of the present disclosure has/have been described in detail with reference to the accompanying drawings, whilst the technical scope of the present disclosure is not limited to the above examples. A person skilled in the art may find various alterations and modifications within the technical scope of the appended claims, and it should be understood that they will naturally come under the technical scope of the present disclosure. 
     For example, the respective steps in the processing carried out by the speech processing apparatus  20  in this specification do not necessarily have to be time-sequentially performed in accordance with the order described as the flowchart. For example, the respective steps in the processing carried out by the speech processing apparatus  20  may be performed in an order different from the order described as the flowchart, or may be performed in parallel. 
     Additionally, a computer program that allows the hardware such as the CPU, the ROM and the RAM incorporated in the speech processing apparatus  20  to demonstrate a function equivalent to that of each configuration of the speech processing apparatus  20  described above can also be created. A storage medium storing the computer program is also provided. 
     Moreover, the effects described in this specification are merely illustrative or exemplary, and not restrictive. That is, with or in the place of the above effects, the technology according to the present disclosure can achieve other effects that are obvious to a person skilled in the art from the description of this specification. 
     Additionally, the present technology may also be configured as below. 
     (1) 
     A speech processing apparatus comprising an analysis unit configured to analyze a meaning of speech uttered by a user based on a recognition result of the speech and an analysis result of a behavior of the user while the user is uttering the speech. 
     (2) 
     The speech processing apparatus according to (1), wherein the analysis unit includes 
     a meaning analysis unit configured to analyze the meaning of the speech uttered by the user based on the recognition result of the speech, and 
     a correction unit configured to correct the meaning obtained by the meaning analysis unit based on the analysis result of the behavior of the user. 
     (3) 
     The speech processing apparatus according to (2), wherein the correction unit determines whether to delete the meaning of the speech corresponding to one speech section in an utterance period of the user based on the analysis result of the behavior of the user in the speech section. 
     (4) 
     The speech processing apparatus according to any one of (1) to (3), wherein the analysis unit uses an analysis result of a change in a visual line of the user as the analysis result of the behavior of the user. 
     (5) 
     The speech processing apparatus according to any one of (1) to (4), wherein the analysis unit uses an analysis result of a change in a facial expression of the user as the analysis result of the behavior of the user. 
     (6) 
     The speech processing apparatus according to any one of (1) to (5), wherein the analysis unit uses an analysis result of a change in an utterance direction as the analysis result of the behavior of the user. 
     (7) 
     The speech processing apparatus according to any one of (1) to (6), wherein the analysis unit further analyzes the meaning of the speech based on a relation between the user and another user indicated by the speech. 
     (8) 
     The speech processing apparatus according to (3), wherein the correction unit further determines whether to delete the meaning of the speech corresponding to the speech section based on whether a particular word is included in the speech section. 
     (9) 
     The speech processing apparatus according to (8), wherein the particular word includes a filler or a negative word. 
     (10) 
     The speech processing apparatus according to any one of (1) to (9), further comprising: 
     a speech input unit to which the speech uttered by the user is input; 
     a speech recognition unit configured to recognize the speech input to the speech input unit; 
     a behavior analysis unit configured to analyze the behavior of the user while the user is uttering the speech; and 
     a processing execution unit configured to execute processing in accordance with the meaning obtained by the analysis unit. 
     (11) 
     A speech processing method comprising analyzing, by a processor, a meaning of speech uttered by a user based on a recognition result of the speech and an analysis result of a behavior of the user while the user is uttering the speech. 
     REFERENCE SIGNS LIST 
       20  SPEECH PROCESSING APPARATUS 
       30  MOBILE TERMINAL 
       220  IMAGE PROCESSING UNIT 
       221  IMAGING UNIT 
       222  FACE IMAGE EXTRACTION UNIT 
       223  EYE FEATURE VALUE EXTRACTION UNIT 
       224  VISUAL LINE IDENTIFICATION UNIT 
       225  FACE FEATURE VALUE EXTRACTION UNIT 
       226  FACIAL EXPRESSION IDENTIFICATION UNIT 
       240  SPEECH PROCESSING UNIT 
       241  SOUND COLLECTION UNIT 
       242  SPEECH SECTION DETECTION UNIT 
       243  SPEECH RECOGNITION UNIT 
       244  WORD DETECTION UNIT 
       245  UTTERANCE DIRECTION ESTIMATION UNIT 
       246  SPEECH FEATURE DETECTION UNIT 
       247  EMOTION IDENTIFICATION UNIT 
       260  ANALYSIS UNIT 
       262  MEANING ANALYSIS UNIT 
       264  STORAGE UNIT 
       266  CORRECTION UNIT 
       280  PROCESSING EXECUTION UNIT