Patent Publication Number: US-2019180747-A1

Title: Voice recognition apparatus and operation method thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is based on and claims priority under 35 U.S.C. § 119 to Korean Patent Application No. 10-2017-0167775, filed on Dec. 7, 2017, in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference herein in its entirety. 
     BACKGROUND 
     1. Field 
     The disclosure relates to a voice recognition apparatus and an operation method thereof, and, for example, to a voice recognition method and apparatus for analyzing a user input based on content being executed and generating and outputting an answer. 
     The disclosure also relates to an artificial intelligence (AI) system for simulating functions such as recognition and determination using a machine learning algorithm such as deep learning and an application thereof. 
     2. Description of Related Art 
     Artificial intelligence (AI) systems are computer systems with intelligence at the human level. Unlike existing rule-based smart systems, AI systems are capable of self-learning, making determinations, and getting smart. As the use of AI systems improves more accurately the recognition rate and understanding of a user&#39;s taste, existing rule-based smart systems have been being gradually replaced by deep learning-based AI systems. 
     AI technology includes machine learning (deep learning) and element technologies using the machine learning. 
     Machine learning may refer to an algorithm technology of self-classifying/learning features of pieces of input data. Element technologies may refer to technologies of simulating functions such as recognition and determination of the human brain using a machine learning algorithm such as deep learning and include linguistic understanding, visual understanding, inference/prediction, knowledge expression, movement control, and the like. 
     Various fields in which the AI technology is applied are as follows. The linguistic understanding is a technique of recognizing and applying/processing human languages/characters and includes natural language processing, machine translation, conversation system, question and answer, voice recognition/synthesis, and the like. The visual understanding is a technique of recognizing and processing an object like a human sense of sight and includes object recognition, object tracking, image search, human recognition, scene understanding, space understanding, image enhancement, and the like. The inference/prediction is a technique of determining information to perform logical inference and prediction and includes knowledge/probability-based inference, optimization prediction, preference-based planning, recommendation, and the like. The knowledge expression is a technique of automatically processing experience information of a human being into knowledge data and includes knowledge construction (data creation/classification), knowledge management (data utilization), and the like. The movement control is a technique of controlling autonomous driving of a vehicle and a motion of a robot and includes motion control (navigation, collision, and driving), operation control (behavior control), and the like. 
     Recently, together with the development of electronic devices, such as a smartphone, for performing a combination of various functions, electronic devices having a voice recognition function to improve maneuverability have been launched. A voice recognition function allows a user of an electronic device to easily control a device through voice without operating a separate button or touching a touch module. 
     For the voice recognition function, various AI technologies may be used. Through linguistic understanding, the content of a user&#39;s utterance may be accurately recognized, and the user&#39;s intention included in the utterance content may be detected. In addition, through visual understanding, content used by the user may be understood, and through the inference/prediction, an appropriate answer according to the user&#39;s intention and the content being executed by the user may be generated. Furthermore, through movement control, the generated answer may be output in an appropriate form. 
     SUMMARY 
     A voice recognition apparatus for analyzing a user input based on content and generating and outputting an answer and an operation method thereof are provided. 
     Additional aspects will be set forth in part in the description which follows and, in part, will be apparent from the description. 
     In accordance with an aspect of the disclosure, an operation method of a voice recognition apparatus includes: receiving an audio signal and performing voice recognition; acquiring content information of content being executed; analyzing a user input based on the content information from a voice recognized by performing the voice recognition; generating an answer based on the analyzed user input and the content information; and outputting the answer. 
     In accordance with another aspect of the disclosure, a voice recognition apparatus includes: a receiver comprising receiving circuitry configured to receive an audio signal; a processor configured to perform voice recognition on the audio signal, to acquire content information of content being executed, to analyze a user input based on the content information from a voice recognized by performing the voice recognition, and to generate an answer based on the analyzed user input and the content information; and an output unit comprising output circuitry configured to output the answer. 
     In accordance with another aspect of the disclosure, a non-transitory computer-readable recording medium having recorded thereon a program including instructions which, when executed by a processor, causes an apparatus to perform operations including: receiving an audio signal and performing voice recognition; acquiring content information of content being executed; analyzing a user input based on the content information from a voice recognized by performing the voice recognition; generating an answer based on the analyzed user input and the content information; and outputting the answer. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other aspects, features, and advantages of certain embodiments of the present disclosure will be more apparent from the following description, taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  is a diagram illustrating a voice recognition method according to an embodiment; 
         FIG. 2  is a block diagram illustrating an example voice recognition method according to an embodiment; 
         FIG. 3  is a flowchart illustrating an operation method of a voice recognition apparatus, according to an embodiment; 
         FIGS. 4, 5 and 6  are diagrams illustrating a voice recognition system according to an embodiment; 
         FIG. 7  is a block diagram illustrating a method used by the voice recognition apparatus to acquire content information, according to an embodiment; 
         FIG. 8  is a block diagram illustrating a method used by the voice recognition apparatus to recognize a user input, according to an embodiment; 
         FIG. 9  is a block diagram illustrating a method used by the voice recognition apparatus to acquire context information, according to an embodiment; 
         FIG. 10  is a block diagram illustrating a method used by the voice recognition apparatus to generate an answer, according to an embodiment; 
         FIG. 11  is a flowchart illustrating a method used by the voice recognition apparatus to determine relevance of an answer, according to an embodiment; 
         FIG. 12  is a block diagram illustrating a method used by the voice recognition apparatus to determine an output form of an answer, according to an embodiment; 
         FIG. 13  is a block diagram illustrating the voice recognition apparatus according to an embodiment; 
         FIG. 14  is a block diagram illustrating the voice recognition apparatus according to an embodiment; 
         FIG. 15  is a diagram illustrating an example of outputting an answer that varies according to content being executed; 
         FIG. 16  is a diagram illustrating an example wherein the voice recognition apparatus outputs an answer that varies according to context information; 
         FIG. 17  is a diagram illustrating another example of outputting an answer that varies according to content being executed; 
         FIG. 18  is a diagram illustrating an example of determining relevance of an answer; 
         FIG. 19  is a diagram illustrating an example form of an answer output according to an embodiment; and 
         FIG. 20  is a diagram illustrating another example wherein the voice recognition apparatus outputs an answer that varies according to context information according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in greater detail to example embodiments, examples of which are illustrated in the accompanying drawings. In the drawings, parts irrelevant to the description may be omitted to clearly describe the example embodiments, and like reference numerals denote like elements throughout the disclosure. In this regard, the present example embodiments may have different forms and should not be understood as being limited to the descriptions set forth herein. Accordingly, the example embodiments are merely described below, by referring to the figures, to explain various aspects. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. Expressions such as “at least one of,” when preceding a list of elements, modify the entire list of elements and do not modify the individual elements of the list. 
     Some embodiments of the present disclosure can be represented with functional blocks and various processing steps. Some or all of these functional blocks can be implemented by various numbers of hardware and/or software configurations for executing specific functions. For example, the functional blocks of the present disclosure can be implemented by one or more microprocessors or by circuit configurations for certain functions. In addition, for example, the functional blocks of the present disclosure can be implemented by various programming or scripting languages. Functional blocks can be implemented with algorithms executed in one or more processors. In addition, the present disclosure may adopt the related art for electronic environment setup, signal processing, and/or data processing. 
     In addition, connection lines or connection members between components shown in the drawings illustrate functional connections and/or physical or circuit connections. In an actual apparatus, connections between components can be represented by replaceable or additional various functional connections, physical connections, or circuit connections. 
     In addition, terms such as “ . . . unit”, “ . . . module”, or the like, may refer to units that perform at least one function or operation, and the units may be implemented as hardware or software or as a combination of hardware and software. “Unit” or “module” may be implemented by a program stored in an addressable storage medium and executable by a processor. 
     For example, a “unit” or “module” may be implemented by components, such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, a database, data structures, tables, arrays, and/or variables, or the like. 
       FIG. 1  is a diagram illustrating a voice recognition method according to an embodiment. 
     Referring to  FIG. 1 , a voice recognition apparatus  100  according to an embodiment may receive an audio signal including a voice signal uttered by a user  10  and perform voice recognition on the voice signal included in the received audio signal. Thereafter, the voice recognition apparatus  100  may understand a user input included in the voice signal and generate and output an answer in response to the user input. Through this process, the user  10  may control the voice recognition apparatus  100  using a voice signal. 
     Furthermore, the voice recognition apparatus  100  according to an embodiment may generate and output an answer suitable for an intention of the user  10  and a situation in which the voice recognition apparatus  100  operates, in addition to uniform analysis and understanding of a user input through a combination of voice recognition technology and artificial intelligence (AI) technology. For example, the voice recognition apparatus  100  according to an embodiment may be a deep learning-based AI system. According to an embodiment, the voice recognition apparatus  100  may recognize, apply, and process a human language and infer and predict a situation in which the voice recognition apparatus  100  operates using the AI technology. 
     In an embodiment, the voice recognition apparatus  100  may receive an audio signal in an idle state or receive an audio signal in a particular situation. For example, the voice recognition apparatus  100  may receive an audio signal while content is being executed. In this case, the voice recognition apparatus  100  may more accurately understand an intention according to utterance of the user  10  by analyzing a user input based on the content being executed in the voice recognition apparatus  100  rather than simply analyzing a user input from a voice recognized by performing voice recognition on an audio signal. Furthermore, even when an answer in response to a user input is generated and output, an appropriate answer may be output in an appropriate form by generating an answer based on the content being executed and a situation in which the voice recognition apparatus  100  operates. 
     Referring to  FIG. 1 , video content  20  in which a panda appears is being executed in the voice recognition apparatus  100 . In this case, the voice recognition apparatus  100  may relatively accurately understand an intention of the user  10  by analyzing a user input based on the video content  20  being executed. For example, when the user  10  wants to know where the panda appearing in the video content  20  lives, the user  10  may ask “where does that panda live?” In this case, in the case of an existing voice recognition apparatus that receives an audio signal and performs voice recognition on the audio signal, the existing voice recognition apparatus cannot detect which panda is “that panda” indicated by a user, and thus, the existing voice recognition apparatus does not accurately understand an intention of the user. On the other hand, the voice recognition apparatus  100  according to an embodiment may understand that the user  10  asks ‘where does the panda appearing in the video content  20  being executed live?’, by analyzing a user input based on the video content  20  being executed. 
     In addition, the voice recognition apparatus  100  may generate an appropriate answer in response to a user input based on the video content  20  being executed. For example, the voice recognition apparatus  100  may infer that ‘the panda appearing in the video content  20  being executed’ lives in “Y zoo”, based on the video content  20  being executed. 
       FIG. 1  illustrates the voice recognition apparatus  100  for receiving an audio signal, understanding a user input included in the received audio signal, and generating and outputting an answer in response to the user input, however, the voice recognition apparatus  100  is not limited thereto and may also receive various input signals such as a gesture, a character, and/or a video, or the like, understand a user input included in a corresponding signal, and generate and output an answer in response to the user input. For example, an electronic device including the voice recognition apparatus  100  may receive a signal such as a gesture, a character, and/or a video, or the like, from a user through various sensors and/or a user interface, understand a user input included in the received signal, and generate and output an answer in response to the user input. 
     Furthermore, the voice recognition apparatus  100  may output an answer of an appropriate form in consideration of a situation in which the voice recognition apparatus  100  operates, the video content being executed, and the like. For example, the voice recognition apparatus  100  may output an answer without interrupting the video content watched by the user  10 , based on a situation in which the voice recognition apparatus  100  operates. A method of outputting an answer will be described in greater detail below. 
       FIG. 2  is a block diagram illustrating a detailed voice recognition method according to an embodiment. 
     The voice recognition method illustrated in  FIG. 2  may include the voice recognition method performed with reference to  FIG. 1 . In addition, hereinafter, as an example, a case where the voice recognition apparatus  100  is executing the video content  20  in which a panda appears, like  FIG. 1 , is described. 
     Referring to  FIG. 2 , the voice recognition apparatus  100  according to an embodiment receives an audio signal including a voice of the user  10  while executing the video content  20  in which a panda appears. For example, when the user  10  asks “where does that panda live?”, the voice recognition apparatus  100  receives an audio signal including a corresponding voice. 
     According to an embodiment, the voice recognition apparatus  100  may perform voice recognition  210  on the received audio signal. The voice recognition apparatus  100  may receive a user input by performing the voice recognition  210  on the received audio signal. For example, when the user  10  asks “where does that panda live?”, the voice recognition apparatus  100  may understand the meaning of the sentence “where does that panda live?” as it is and receive the meaning of the sentence as a user input. The voice recognition  210  may, for example, be a process of uniformly analyzing and understanding a voice signal of the user  10 . 
     In this case, the voice recognition apparatus  100  may recognize, apply, and process a language of a speaker, which is included in an audio signal, using AI technology. In an embodiment, the voice recognition apparatus  100  may increase accuracy of the voice recognition  210  through learning (e.g., deep learning) of personal characteristics of the user  10 , for example, intonation, linguistic habits, frequently using words and expressions, and the like, or linguistic understanding of a voice signal, for example, natural language processing and the like. 
     According to an embodiment, the voice recognition apparatus  100  may acquire via content information acquisition  220  content information of content being executed, e.g., the video content  20  in which the panda appears. The content information may include story information and execution information of the content being executed. The story information of the content may include information regarding the content itself, such as a synopsis of the content, a state of an object included in the content, a relationship between objects, and the like. In addition, the execution information of the content may include information indicating an execution state of the content, such as a name of the content and a progress of the content. For example, the voice recognition apparatus  100  may acquire information regarding a synopsis, animals and plants in the content, a relationship between a human being and the animals and plants, a name of the video content  20 , a total play time, a current progress, and the like with respect to the content being executed. 
     In this case, the voice recognition apparatus  100  may acquire via the content information acquisition  220  wide and deep content information using AI technology. For example, the voice recognition apparatus  100  may increase a degree of understanding with respect to a scene of the content being executed, through visual understanding with respect to the content. 
     In addition, the content information acquisition  220  may be performed along with the voice recognition  210  on the audio signal in parallel or sequentially. When the content information acquisition  220  and the voice recognition  210  are performed sequentially, the voice recognition  210  or the content information acquisition  220  may be first performed. In addition, the content information acquisition  220  may be performed continuously when the content is being executed, regardless of other processes. According to an embodiment, the voice recognition apparatus  100  may acquire the content information by analyzing the content being executed or acquire information related to the content being executed from the outside, e.g., a server, a social network service (SNS), a database, or the like. 
     According to an embodiment, the voice recognition apparatus  100  may analyze via a user input analysis  230  a user input based on a voice recognition result and the acquired content information. Herein, the user input analysis  230  is provided to more accurately understand an utterance intention of the user  10  than the uniform analysis and understanding of the voice signal of the user  10  in the voice recognition  210 . For example, the meaning of the sentence “where does that panda live?” is understood as it is through the voice recognition  210 , whereas it may be understood based on the acquired content information through the user input analysis  230  that the user  10  asks ‘where does the panda appearing in the video content  20  being executed live?’. 
     The voice recognition apparatus  100  may more accurately understand a user input using AI technology than a case where the AI technology is not used. For example, the voice recognition apparatus  100  may relatively accurately understand a user input by logically inferring and predicting an intention of the user  10  based on the content information. 
     According to an embodiment, the voice recognition apparatus  100  may generate via answer generation  240  an answer based on the acquired content information in response to the recognized user input. The voice recognition apparatus  100  may generate an appropriate answer desired by the user  10 , based on the acquired content information. In this case, the voice recognition apparatus  100  may take into account not only the content information but also context information of the voice recognition apparatus  100 . The context information of the voice recognition apparatus  100  may include information related to a location of the voice recognition apparatus  100 , a motion, a peripheral environment, whether the voice recognition apparatus  100  is connected to another electronic device, the characteristics of a user who uses the voice recognition apparatus  100 , and the like. 
     In addition, the voice recognition apparatus  100  may use various methods for the answer generation  240 . For example, the voice recognition apparatus  100  may determine a type of the content through the content information, generate an answer through a movie analysis scheme such as plot analysis, character analysis, and scenario analysis when the content is movie, and generate an answer through a music analysis scheme such as a music information retrieval system, chord analysis, and tonality analysis when the content is music. Since an answer generated through this process is generated by understanding a user input really intended by the user  10 , the answer has a high (e.g., greater) possibility of being an answer desired by the user  10 . For example, the voice recognition apparatus  100  may acquire, through the content information, information indicating that the content being executed is a documentary related to the life of a panda, detect, from a database, that a scene being viewed by the user  10  was taken at the Y zoo, and generate an answer “the panda lives in the Y zoo.” 
     In this case, the voice recognition apparatus  100  may generate a best answer using AI technology. For example, the voice recognition apparatus  100  may generate an answer closest to an answer desired by the user  10  or determine relevance of the answer, using a database through knowledge management. Herein, the database may be a knowledge-based database. 
     According to an embodiment, the voice recognition apparatus  100  may output the generated answer. In this case, the voice recognition apparatus  100  may output an answer of an appropriate form in consideration of the context information of the voice recognition apparatus  100  and the like. For example, the voice recognition apparatus  100  may output the answer as subtitles instead of voice when a commentary on the life of a panda is being output in the content being executed or the user  10  is in an environment in which the answer cannot be properly heard since the area around the voice recognition apparatus  100  is noisy, at a time point where the answer is output. In this case, the voice recognition apparatus  100  may determine a relatively appropriate output form of the answer using AI technology. 
     Hereinafter, a voice recognition method and a voice recognition apparatus according to various example embodiments of the present disclosure will be described with reference to the accompanying drawings. As described above, the voice recognition method and the voice recognition apparatus according to embodiments of the present disclosure may perform voice recognition based on content being executed and provide, in an appropriate form, an appropriate answer according to the voice recognition. 
       FIG. 3  is a flowchart illustrating an operation method of a voice recognition apparatus, according to an embodiment. 
     Referring to  FIG. 3 , in operation  310 , the voice recognition apparatus  100  receives an audio signal and performs voice recognition on the received audio signal. 
     In operation  320 , the voice recognition apparatus  100  acquires content information of content being executed. The content information may include story information and execution information of the content being executed. 
     Although  FIG. 3  illustrates that operation  310  is performed and then operation  320  is performed, operations  310  and  320  may be performed sequentially or in parallel, and when operations  310  and  320  are performed sequentially, operation  320  may be performed and then operation  310  may be performed. 
     Furthermore, although not shown in  FIG. 3 , the voice recognition apparatus  100  may further perform an operation of acquiring context information related to a situation in which the voice recognition apparatus  100  operates. 
     In operation  330 , the voice recognition apparatus  100  analyzes a user input based on the content information from a voice recognized by performing the voice recognition. According to an embodiment, the voice recognition apparatus  100  may perform natural language understanding with respect to the recognized voice based on the content information. In greater detail, the natural language understanding with respect to the recognized voice may be performed, and then the natural language understanding with respect to the recognized voice may be corrected based on the content information. In addition, the voice recognition apparatus  100  may analyze the user input based on the context information. 
     In operation  340 , the voice recognition apparatus  100  generates an answer based on the analyzed user input and the content information. According to an embodiment, the voice recognition apparatus  100  may determine relevance of the answer, and correct the answer when it is determined that the answer is not relevant. In this case, universal relevance, user relevance, and the like of the answer may be determined. Alternatively, the voice recognition apparatus  100  may generate an answer based on the context information. 
     In operation  350 , the voice recognition apparatus  100  outputs the answer. According to an embodiment, the voice recognition apparatus  100  may determine an output form of the answer based on the content information and the context information. 
     According to an embodiment, the voice recognition apparatus  100  may perform the voice recognition based on the content being executed and provide, in an appropriate form, an appropriate answer according to the voice recognition. 
       FIGS. 4, 5 and 6  are diagrams illustrating a voice recognition system according to an embodiment. 
     As illustrated in  FIG. 4 , the voice recognition system according to an embodiment may include the voice recognition apparatus  100 . For example, the voice recognition apparatus  100  may be a mobile computing device or a non-mobile computing device, such as, for example, and without limitation, a smartphone, a tablet personal computer (PC), a PC, a smart TV, a personal digital assistant (PDA), a laptop computer, a media player, a micro server, a global positioning system (GPS) device, a wearable device, an electronic book terminal, a digital broadcasting terminal, a navigation machine, a kiosk, an MP3 player, a digital camera, an electronic control device, a central information display (CID) in a vehicle, or the like, but is not limited thereto. The voice recognition system may not be an independent device but be included as one component in another device. For example, the voice recognition system may be included in a smartphone or included in an electronic control device or a CID included in a vehicle. 
     The voice recognition apparatus  100  according to an embodiment may receive an audio signal including a voice signal uttered by the user  10  and perform voice recognition on the voice signal included in the received audio signal. Thereafter, the voice recognition apparatus  100  may understand a user input from the recognized voice and generate and output an answer in response to the user input. 
     Alternatively, as illustrated in  FIG. 5 , the voice recognition system according to an embodiment may include a wearable device  100 - 1  and an electronic device  110  connected to (e.g., coupled with) the wearable device  100 - 1 . The wearable device  100 - 1  and the electronic device  110  may be connected in a wired or wireless manner. 
     For example, the wearable device  100 - 1  may be a mobile computing device or a non-mobile computing device having, for example, and without limitation, a shape of a watch, a band, glasses, headphones, earphones, or the like, but is not limited thereto. In addition, the electronic device  110  connected to the wearable device  100 - 1  may be a mobile computing device or a non-mobile computing device, such as, for example, and without limitation, a smartphone, a tablet PC, a PC, a smart TV, a PDA, a laptop computer, a media player, a micro server, a GPS device, a wearable device, an electronic book terminal, a digital broadcasting terminal, a navigation machine, a kiosk, an MP3 player, a digital camera, a CID or an electronic control device in a vehicle, or the like, but is not limited thereto. 
     The wearable device  100 - 1  according to an embodiment may receive an audio signal including a voice signal uttered by the user  10  and transmit the received audio signal to the electronic device  110 . Alternatively, the wearable device  100 - 1  may receive an audio signal including a voice signal uttered by the user  10  and transmit, to the electronic device  110 , a voice signal detected from the received audio signal. Alternatively, the wearable device  100 - 1  may receive an audio signal including a voice signal uttered by the user  10  and transmit, to the electronic device  110 , a feature of the voice signal detected from the received audio signal. It will be understood that the foregoing alternatives are merely examples, and that the present disclosure is not limited thereto. 
     The electronic device  110  may perform voice recognition based on a signal received from the wearable device  100 - 1 . For example, the electronic device  110  may perform voice recognition on a voice signal detected from an audio signal input from the wearable device  100 - 1 . The electronic device  110  may output an answer according to the voice recognition or transmit a voice recognition result to the wearable device  100 - 1  such that the wearable device  100 - 1  outputs the answer according to the voice recognition. 
     Alternatively, as illustrated in  FIG. 6 , the voice recognition system according to an embodiment may include a voice recognition apparatus  100 - 2  and a server  120  connected to (e.g., in operative communication with) the voice recognition apparatus  100 - 2 . The voice recognition apparatus  100 - 2  and the server  120  may be connected to each other in a wired or wireless manner. 
     The voice recognition apparatus  100 - 2  may transmit an audio signal including a voice signal uttered by the user  10 , the voice signal detected from the audio signal, or a feature of the voice signal to the server  120 , and the server  120  may receive the audio signal, the voice signal, or the feature and perform voice recognition. In addition, the server  120  may transmit a voice recognition result to the voice recognition apparatus  100 - 2  such that the voice recognition apparatus  100 - 2  outputs an answer according to the voice recognition. 
     The voice recognition system according to an embodiment may be a deep learning-based AI system. Using AI technology, the voice recognition system according to an embodiment may recognize and process a human language by inferring and predicting a situation in which a voice recognition apparatus operates. 
     Hereinafter, various example operation methods of a voice recognition apparatus, according to embodiments, will be described in greater detail with reference to  FIGS. 7 to 12 . Herein, like in the case of  FIG. 1 , a case where the video content  20 , in which a panda appears, is being executed is described as an example. 
       FIG. 7  is a block diagram illustrating a method used by the voice recognition apparatus  100  to acquire content information, according to an embodiment. 
     As described above, the content information may include content story information  710  related to a story of content being executed and content execution information  720  related to content execution. Referring to  FIG. 7 , the voice recognition apparatus  100  according to an embodiment may acquire the content information using analysis  701  of the content being executed and databases  702  and  703 . For example, the databases  702  and  703  may include a content-related database  702  including data on a story of content, a format of the content, and the like and a content execution state-related database  703  including data indicating a state of content being executed. 
     The content-related database  702  may, for example, be a knowledge-based database, and may be stored in the voice recognition apparatus  100  or stored in an external device such that the voice recognition apparatus  100  uses the content-related database  702  by accessing the external device through a communication interface (not shown). The content execution state-related database  703  is a database generated, stored, and managed according to execution of content, and thus, the content execution state-related database  703  may be stored in a device for executing the content or in another device connected to the device for executing the content. Therefore, when the voice recognition apparatus  100  executes content, the content execution state-related database  703  may be stored in the voice recognition apparatus  100  or in a storage device connected to the voice recognition apparatus  100 . 
     The voice recognition apparatus  100  according to an embodiment may analyze  701  content being executed and acquire information related to the content being executed from the content-related database  702 , thereby acquiring the content story information  710 . Referring to  FIG. 7 , the voice recognition apparatus  100  may perform visual understanding with respect to the content being executed using AI technology, analyze the content being executed using a scenario analysis scheme, and understand a story of the content using metadata or the like of the content. For example, the voice recognition apparatus  100  may detect, through visual understanding, that a panda appears in the content and detect, through scenario analysis, that the content is related to the life of a panda in a zoo. In addition, the voice recognition apparatus  100  may acquire information indicating that a name of a program is ‘go to zoo’, a broadcasting episode number is ‘episode  12 ’, and a story of the episode is ‘study of panda life in the zoo’. 
     As described above, the acquired content story information  710  may include information regarding the content itself, such as a synopsis of the content, a state of an object included in the content, and a relationship between objects. For example, referring to  FIG. 7 , according to the content being executed in the voice recognition apparatus  100 , there appear a panda in an indoor pen and people carrying a camera outside the pen, and the people are spectators who are viewing the panda in a zoo. 
     The voice recognition apparatus  100  according to an embodiment may acquire the content execution information  720  from the content-related database  702  and the content execution state-related database  703 . As described above, referring to  FIG. 7 , the voice recognition apparatus  100  may acquire, from the content-related database  702 , information indicating that a name of a program being executed is ‘go to zoo’, a broadcasting episode number is ‘episode  12 ’, and a story of the episode is ‘study of panda life in the zoo’. In addition, the voice recognition apparatus  100  may acquire, from the content execution state-related database  703 , information indicating that a play time of the content being executed is one hour, 12 minutes of the play time have elapsed, a sound is output at 80/100, subtitles exist but are not used at present, and the content being executed is a video on demand (VOD). 
     As described above, the acquired content execution information  720  may include a synopsis progress, a main story, and the like. Referring to  FIG. 7 , according to the content being executed in the voice recognition apparatus  100 , the synopsis progress is 100/500 in the total story, and the main story is related to the life of the panda in the zoo. 
       FIG. 8  is a block diagram illustrating a method used by the voice recognition apparatus  100  to recognize a user input, according to an embodiment. 
     The voice recognition apparatus  100  according to an embodiment receives an audio signal including a voice signal uttered by the user  10  and performs voice signal analysis  810  on the voice signal included in the received audio signal. For example, referring to  FIG. 8 , when the user  10  asks “where does that panda live?”, the voice recognition apparatus  100  may extract only a voice signal of the user  10 , “where does that panda live?”, from the received audio signal. In this process, the voice recognition apparatus  100  may detect, through the voice signal analysis  810 , that the user  10  is a male, an age of the user  10  is about thirties, and an emotional state of the user  10  is calm. 
     The voice recognition apparatus  100  according to an embodiment may perform sentence analysis  820  to perform natural language understanding  830  with respect to the voice signal. In a process of the sentence analysis  820 , to perform the natural language understanding  830 , sentence symbols are processed, and a sentence is segmented. Referring to  FIG. 8 , the voice signal of the user  10 , “where does that panda live?”, is segmented into [(anaphora: that)/(subject: animal: panda)/(question: where)/(object: live)]. Thereafter, the voice recognition apparatus  100  according to an embodiment may understand the sentence by performing the natural language understanding  830  with respect to the analyzed sentence. 
     The process of the sentence analysis  820  and the natural language understanding  830  illustrated in  FIG. 8  is merely illustrative, and natural language understanding may be performed by a method other than the method shown in  FIG. 8 . In addition, the voice recognition apparatus  100  may recognize, apply, and process a language of a speaker, which is included in an audio signal, using AI technology. In more detail, the voice recognition apparatus  100  may increase accuracy of voice recognition through learning (deep learning) of personal characteristics of the user  10 , for example, intonation, linguistic habits, frequently using words and expressions, and the like, or linguistic understanding of a voice signal, for example, natural language processing and the like. 
     According to an embodiment, the voice recognition apparatus  100  performs contents shortage and/or ambiguity analysis  840  with respect to a voice recognition result. This process is to determine whether the voice recognition apparatus  100  has accurately understood a user input through the voice recognition. As a result of analyzing the voice recognition result, when the contents contained in the voice signal are insufficient or ambiguous for the voice recognition apparatus  100  to accurately understand the user input, the voice recognition apparatus  100  may perform natural language understanding result correction  850  using content information. That is, the user input recognized through the voice recognition may be corrected. 
     In greater detail, as shown in  FIG. 7 , the voice recognition apparatus  100  may acquire content information including the content story information  710  and the content execution information  720  of the content being executed. The voice recognition apparatus  100  may relatively accurately understand an utterance intention of the user  10 , e.g., a user input actually intended by the user  10 , by supplementing or clearing, through the content information, the contents contained in the voice signal. 
     For example, referring to  FIG. 8 , the voice recognition apparatus  100  may understand, based on the content story information  710  and the content execution information  720  of the content being executed, that a question of the user  10 , which is actually intended by the user  10 , is asking where ‘a panda in a pen, which appears at a time point of 100/500 in episode  12  of a program “go to a zoo”’ lives. In addition, the voice recognition apparatus  100  may relatively accurately understand an intention of the user  10  using AI technology. For example, the voice recognition apparatus  100  may relatively accurately understand a user input by logically inferring and predicting the intention of the user  10  based on the content information. 
       FIG. 9  is a block diagram illustrating a method used by the voice recognition apparatus  100  to acquire context information, according to an embodiment. 
     The voice recognition apparatus  100  according to an embodiment may acquire context information  910  related to a situation in which the voice recognition apparatus  100  operates. Referring to  FIG. 9 , the voice recognition apparatus  100  according to an embodiment may acquire the context information  910  using a detector (e.g., including detection circuitry)  920  configured to detect external information, a user profile  901 , and the like. 
     The voice recognition apparatus  100  may receive and detect various pieces of information for determining a situation in which the voice recognition apparatus  100  operates, using information detected by the detector  920 . For example, the detector  920  may receive or detect a location of the voice recognition apparatus  100 , information related to a motion, information on a user who uses the voice recognition apparatus  100 , a peripheral environment of the voice recognition apparatus  100 , and the like. 
     The user profile  901  may, for example, refer to the information on the user who uses the voice recognition apparatus  100  and may be stored in the voice recognition apparatus  100  or received from an external device through a communication interface (not shown). In addition, the voice recognition apparatus  100  may update the user profile  901  based on user information acquired through the detector  920 . 
       FIG. 10  is a block diagram illustrating a method used by the voice recognition apparatus  100  to generate an answer, according to an embodiment. 
     Referring to  FIG. 10 , the voice recognition apparatus  100  according to an embodiment may generate an answer using answer generation  1010  in response to a user command according to a recognized user input. Herein, the answer may include an operation of the voice recognition apparatus  100  according to the user input. 
     The voice recognition apparatus  100  may generate an answer based on content information including the content story information  710  and the content execution information  720  of content being executed and may also generate the answer based on the context information  910  of the voice recognition apparatus  100 . In addition, the voice recognition apparatus  100  may use a database to generate the answer. 
     For example, to generate an answer to a question of a user, “where does that panda live?”, the voice recognition apparatus  100  may acquire a name, an address, or the like of a zoo in which ‘a panda in a pen, which appears at a time point of 100/500 in episode  12  of a program “go to zoo”’ is raised. In addition, when a user asks a story of a drama being broadcast on a TV at present, the voice recognition apparatus  100  may generate an answer by acquiring, from content information or a database, only a story after a time point where the user viewed. The voice recognition apparatus  100  may generate an answer suitable for age limit for a teenager user using user characteristic information included in the context information  910 , thereby generating a user-adaptive answer. 
     Furthermore, the voice recognition apparatus  100  may generate a best answer using AI technology. For example, the voice recognition apparatus  100  may generate an answer closest to an answer desired by the user, using a database through knowledge management. 
       FIG. 11  is a flowchart illustrating a method used by the voice recognition apparatus  100  to determine relevance of an answer, according to an embodiment. 
     The voice recognition apparatus  100  according to an embodiment may determine relevance of a generated answer, and may correct the answer when it is determined that the answer is not appropriate. Even though the answer itself is a right answer, when it is determined that outputting the answer is rather not helpful to a user, the answer may be corrected. 
     Referring to  FIG. 11 , in operation  1110 , the voice recognition apparatus  100  may analyze universal relevance of a generated answer based on a relevance determination database  1101 . The relevance determination database  1101  stores data related to criteria considered to be appropriate based on universal validity and may be learned and updated using AI technology. The relevance determination database  1101  may be stored in the voice recognition apparatus  100  or may be stored in an external device such that the voice recognition apparatus  100  uses the relevance determination database  1101  by accessing the external device through a communication interface (not shown). 
     For example, an answer to a question such as “method of stopping others?” is not universally valid from social and ethical points of view, and thus, it may be analyzed that the answer is not appropriate. In addition, when an answer to a question of a user who is watching a movie may be a spoiler since the answer contains information regarding a following synopsis, outputting the answer may rather interrupt the user from watching the movie, and thus, it may be analyzed that the answer is not appropriate from a general point of view. 
     In addition, according to an embodiment, when universal relevance of an answer is analyzed, the voice recognition apparatus  100  may score a corresponding answer according to preset criteria. The score of the corresponding answer may be used to determine relevance in operation  1130 . 
     In operation  1120 , the voice recognition apparatus  100  may analyze personal relevance of the generated answer based on the user profile  901 . As described above, the user profile  901  is information on a user who uses the voice recognition apparatus  100  and may include data related to personal criteria of the user. The user profile  901  may also be learned and updated using AI technology. 
     For example, in the example described above, it may be analyzed that the spoiler is not appropriate in a general point of view, but for a user who desires to watch movie after understanding a total synopsis, it does not matter that an answer includes a spoiler. 
     In addition, according to an embodiment, when personal relevance of an answer is analyzed, the voice recognition apparatus  100  may score a corresponding answer according to preset criteria. The score of the corresponding answer may be used to determine relevance in operation  1130 . 
     Although  FIG. 11  illustrates that the operation  1110  is performed first and then the operation  1120  is performed, the operations  1110  and  1120  may be performed sequentially or in parallel, and when the operations  1110  and  1120  are performed sequentially, the operation  1120  may be performed first and then the operation  1110  may be performed. 
     In operation  1130 , the voice recognition apparatus  100  determines relevance of a generated answer based on analysis results of the previous operations. According to an embodiment, the voice recognition apparatus  100  may determine whether a corresponding answer is appropriate, according to scores of the scored answer in the universal relevance analysis and the personal relevance analysis. In this case, the voice recognition apparatus  100  may have a preset criterion, and a corresponding criterion may be score. 
     When it is determined that the generated answer is appropriate, the voice recognition apparatus  100  may output the generated answer, and when it is determined that the generated answer is not appropriate, the voice recognition apparatus  100  may proceed to operation  1140  to correct the answer and output the corrected answer. According to an embodiment, the voice recognition apparatus  100  may correct the answer in a way of satisfying the preset criterion, that is, a way of satisfying a set score. 
     In addition, when it is determined that the answer is not appropriate or, the answer is corrected, the voice recognition apparatus  100  may allow the user to select whether the generated answer is output as it is or the corrected answer is output, and output an answer according to the selection of the user. 
     The voice recognition apparatus  100  may generate a best answer using AI technology. For example, the voice recognition apparatus  100  may determine relevance of an answer using a database through knowledge management. 
       FIG. 12  is a block diagram illustrating a method used by the voice recognition apparatus  100  to determine an output form of an answer, according to an embodiment. 
     The voice recognition apparatus  100  according to an embodiment may determine an output form of an answer. In greater detail, the voice recognition apparatus  100  may determine an output form of an answer  1210  based on content information including the content story information  710  and the content execution information  720  of content being executed. In addition, the voice recognition apparatus  100  may determine an answer form based on the context information  910  of the voice recognition apparatus  100 . 
     For example, when a generated answer is not appropriate to be output to the public at a public place since the generated answer includes violent or suggestive contents, the voice recognition apparatus  100  may determine an output form of the answer by outputting the answer through a voice only when a user uses earphones or outputting an image or a video with a reduced size when the answer is output as the image or video. In addition, the voice recognition apparatus  100  may output an answer as subtitles instead of voice when a commentary of a life of a panda is being output in the content being executed or the user is in an environment in which the answer cannot be properly heard since the periphery of the voice recognition apparatus  100  is noisy, at a time point where the answer is output. 
     The voice recognition apparatus  100  may determine an output form of an answer using AI technology. For example, the voice recognition apparatus  100  may recognize a situation in which the voice recognition apparatus  100  operates, from detected external information, and determine an appropriate output form according to the recognized situation. 
       FIG. 13  is a block diagram illustrating the voice recognition apparatus  100  according to an embodiment, and  FIG. 14  is a block diagram illustrating the voice recognition apparatus  100  according to an embodiment. 
     As illustrated in  FIG. 13 , the voice recognition apparatus  100  according to an embodiment may include a receiver (e.g., including receiving circuitry)  1310 , a processor (e.g., including processing circuitry)  1320 , and an output unit (e.g., including output circuitry)  1330 . However, the voice recognition apparatus  100  may be implemented by more components than the components shown in  FIG. 13 . For example, as illustrated in  FIG. 14 , the voice recognition apparatus  100  according to an embodiment may further include at least one component among a memory  1340 , a communication interface (e.g., including communication interface circuitry)  1350 , a user input interface (e.g., including user input circuitry)  1360 , and a detector (e.g., including detection circuitry)  1370 . 
     The voice recognition apparatus  100  according to an embodiment may be included in at least one of, for example, and without limitation, a non-mobile computing device, a mobile computing device, an electronic control device in a vehicle, a server or connected to at least one of the non-mobile computing device, the mobile computing device, the electronic control device in a vehicle, and the server in a wired or wireless manner, or the like, but is not limited thereto. 
     The receiver  1310  according to an embodiment may include various receiving circuitry to receive an audio signal. For example, the receiver  1310  may directly receive an audio signal by converting an external sound into electrical acoustic data using a microphone. The receiver  1310  may receive an audio signal transmitted from an external device. Although  FIG. 13  shows that the receiver  1310  is included in the voice recognition apparatus  100 , according to another embodiment, the receiver  1310  may be included in a separate device and connected to the voice recognition apparatus  100  in a wired or wireless manner. 
     The processor  1320  according to an embodiment may include various processing circuitry and control a general operation of the voice recognition apparatus  100 . For example, the processor  1320  may control the receiver  1310  and the output unit  1330 . In addition, the processor  1320  may control an operation of the voice recognition apparatus  100  using AI technology. 
     The processor  1320  according to an embodiment may analyze a user command based on content being executed and provide an appropriate answer according to the analysis in an appropriate form. In greater detail, the processor  1320  may perform voice recognition on an audio signal, acquire content information of the content being executed, analyze a user input based on the content information from a voice recognized by performing the voice recognition, and generate an answer based on the analyzed user input and the content information. In addition, the processor  1320  may output the generated answer through the output unit  1330  in an appropriate form. 
     The content information may include, for example, and without limitation, story information and execution information of the content being executed. For example, the story information of the content may include information regarding the content itself, such as a synopsis of the content, a state of an object included in the content, and a relationship between objects. In addition, the execution information of the content may include information indicating an execution state of the content, such as a name of the content and a progress of the content. The processor  1320  may acquire the content information by analyzing and processing the content being executed or acquire the content information from an external device. Therefore, as illustrated in  FIG. 14 , the voice recognition apparatus  100  may further include a communication interface  1350 . 
     The processor  1320  according to an embodiment may perform natural language understanding with respect to the recognized voice based on the content information. The processor  1320  may perform natural language understanding with respect to the recognized voice and correct the natural language understanding with respect to the recognized voice based on the content information. 
     The processor  1320  according to an embodiment may determine relevance of the answer and may correct the answer when it is determined that the answer is not appropriate. In this case, the processor  1320  may determine universal relevance and/or user relevance of the answer. 
     The processor  1320  according to an embodiment may control the detector  1370  to acquire context information related to a situation in which the voice recognition apparatus  100  operates and generate the answer based on the context information. The context information may include information related to a location of the voice recognition apparatus  100 , a motion, a peripheral environment, whether the voice recognition apparatus  100  is connected to another electronic device, the characteristics of a user who uses the voice recognition apparatus  100 , and the like. In addition, the processor  1320  may analyze the user input based on the context information. 
     The processor  1320  according to an embodiment may determine an output form of the answer based on the content information and/or the context information. 
     The processor  1320  according to an embodiment may be implemented by hardware and/or software configurations that execute certain functions. For example, the processor  1320  may include at least one of a content information acquirer (not shown) configured to acquire content information of content being executed, a voice recognizer (not shown) configured to perform voice recognition on an audio signal, a user command recognizer (not shown) configured to a user command included in the audio signal based on the content information, and an answer generator (not shown) configured to an answer in response to the user command based on the content information. Each of the foregoing functional elements may include various processing circuitry and/or program elements. 
     For example, the functions performed by the processor  1320  according to an embodiment may be implemented by at least one microprocessor or circuit configurations for corresponding functions. Some or all of the functions performed by the processor  1320  may be implemented by a software module configured using various programming languages or a script language to be executed by the processor  1320 . Although  FIGS. 13 and 14  show that the voice recognition apparatus  100  includes one processor  1320 , the voice recognition apparatus  100  is not limited thereto and may include a plurality of processors. 
     The output unit  1330  according to an embodiment may include various output circuitry and output a result of performing the voice recognition on the audio signal. The output unit  1330  may inform the user of the result of performing the voice recognition or transmit the result of performing the voice recognition to an external device (e.g., a smartphone, a smart TV, a smart watch, a server, or the like). 
     In greater detail, the output unit  1330  according to an embodiment may output an answer corresponding to the result of performing the voice recognition. For example, the voice recognition apparatus  100  may output an answer to a question of the user, through the output unit  1330 . In addition, the voice recognition apparatus  100  may determine a function of the voice recognition apparatus  100 , which corresponds to the result of performing the voice recognition, and output, through the output unit  1330 , a screen image for performing a corresponding function. Alternatively, the voice recognition apparatus  100  may transmit a keyword corresponding to the result of performing the voice recognition to an external server, receive information related to the transmitted keyword from the server, and output the received information on a screen through the output unit  1330 . 
     The output unit  1330  according to an embodiment may output information received from the outside, processed by the processor  1320 , or stored in the voice recognition apparatus  100 , in at least one form of light, sound, image, and vibration. For example, the output unit  1330  may further include at least one of a display configured to output a text or an image, a sound output unit configured to output a sound, and a vibration motor configured to output vibrations. 
     As illustrated in  FIG. 14 , the voice recognition apparatus  100  according to an embodiment may further include at least one component among the memory  1340 , the communication interface  1350 , the user input interface  1360 , and the detector  1370 . 
     The memory  1340  according to an embodiment may include at least one type of storage medium among a flash memory type memory, a hard disk type memory, a multimedia card micro type memory, a card type memory (e.g., a secure digital (SD) memory, an extreme digital (XD) memory, or the like), random access memory (RAM), static RAM (SRAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), programmable ROM (PROM), a magnetic memory, a magnetic disc, and an optical disc. 
     The user input interface  1360  according to an embodiment may include various user input interface circuitry and receive a user input for controlling the voice recognition apparatus  100 . The user input interface  1360  may include a user input device including input circuitry comprising, for example, and without limitation, a touch panel configured to receive a touch of the user, a button configured to receive a push operation of the user, a wheel configured to receive a rotational operation of the user, a keyboard, a dome switch, and the like, but is not limited thereto. 
     The communication interface  1350  according to an embodiment may include various communication interface circuitry and communicate with an external electronic device or server through wired or wireless communication. The communication interface  1350  may acquire content information, context information of the voice recognition apparatus  100 , and the like. For example, the communication interface  1350  may communicate with an external device having a database to acquire the content information, context information of the voice recognition apparatus  100 , and the like. 
     The communication interface  1350  according to an embodiment may include, for example, and without limitation, a near-field communication module, a wired communication module, a mobile communication module, a broadcast reception module, and the like. 
     The detector  1370  may include various detecting circuitry, such as, for example, and without limitation, one or more sensors and receive and detect various pieces of information for determining a situation in which the voice recognition apparatus  100  operates. For example, the detector  1370  may receive or detect a location of the voice recognition apparatus  100 , information related to a motion, information on the user who uses the voice recognition apparatus  100 , a peripheral environment of the voice recognition apparatus  100 , and the like. 
     For example, the detector  1370  may include, for example, and without limitation, an illumination sensor, a biosensor, a tilt sensor, a position sensor, a proximity sensor, a geomagnetic sensor, a gyroscope sensor, a temperature/humidity sensor, an infrared sensor, and/or a velocity/acceleration sensor, or the like, taken at least one or in combination. 
     According to an embodiment, voice recognition may be performed based on content being executed, and an appropriate answer according to the voice recognition may be provided in an appropriate form. 
     The voice recognition method or the voice recognition apparatus  100  according to embodiments has been described. Hereinafter, examples of using the voice recognition method or the voice recognition apparatus  100  according to embodiments will be described with reference to  FIGS. 15 to 20 . 
       FIG. 15  is a diagram illustrating an example of outputting an answer that varies according to content being executed. 
     As illustrated in  FIG. 15 , the voice recognition apparatus  100  may output an answer that varies according to content being executed, with respect to a voice signal of the user  10 . Referring to  FIG. 15 , for example, when the user  10  asks a question “what is 11?”, the voice recognition apparatus  100  may analyze and understand the meaning of the question, which varies according to content being executed. 
     When the user  10  is watching a baseball game, the voice recognition apparatus  100  may acquire information on a baseball player whose back number is 11 and output an answer “the player whose back number is 11 in a baseball team A is X”. When the user  10  is watching a soccer game, the voice recognition apparatus  100  may acquire information on a soccer player whose back number is 11 and output an answer “the player whose back number is 11 in a soccer team B is Y”. 
     When the user  10  is watching a channel guide or changing a channel, the voice recognition apparatus  100  may acquire information on a channel number 11 and output an answer “channel number 11 is a C broadcasting station, and a program D is being broadcast at present”. Furthermore, when the user  10  is watching a music video or performance of a certain singer in a music program, the voice recognition apparatus  100  may acquire information on a track number 11 of a corresponding album of the singer and output an answer “Track number 11 is E. Do you want to listen to it?”. 
     According to an embodiment, voice recognition may be performed based on content being executed, and an appropriate answer according to the voice recognition may be provided. 
       FIG. 16  is a diagram illustrating an example wherein the voice recognition apparatus  100  outputs an answer that varies according to context information. 
     Referring to  FIG. 16 , the user  10  asks a synopsis of content being executed. In general, since the user  10  will know the synopsis of a portion which the user  10  has watched, there is a high possibility that an intention of the user  10  is asking the contents of a portion which the user  10  has not watched. In this case, an existing voice recognition apparatus uniformly understands a question of the user  10 , “I&#39;m late a little. What was the story?” and outputs, as an answer, a total synopsis of the content being executed, “in the summer of 1998, a main character ‘X’ . . . ”. Therefore, the user  10  should order again “no, only up to now from the portion I watched”. 
     Compared with the existing voice recognition apparatus, the voice recognition apparatus  100  according to an embodiment may acquire only the contents after a time point where the user  10  watched using user characteristic information included in the context information  910  of the voice recognition apparatus  100  and output an answer suitable for an intention of the user  10 , “a wedding ceremony of ‘X’ was performed”. 
     According to an embodiment, the voice recognition apparatus  100  may quickly and conveniently output an answer suitable for an intention of the user  10 . 
       FIG. 17  is a diagram illustrating another example of outputting an answer that varies according to content being executed. 
     Referring to  FIG. 17 , the user  10  asks the same question in the middle of watching different pieces of content in situations of dialogs  1  and  2 . In the situation of dialog  1 , the user  10  is watching a rare animal documentary, and in the situation of dialog  2 , the user  10  is watching a zoo-related program. 
     The voice recognition apparatus  100  according to an embodiment may output an answer that varies according to content being executed even when the voice recognition apparatus  100  receives the same question from the user  10 . In the situation of dialog  1  in which the user  10  is watching the rare animal documentary, the voice recognition apparatus  100  may acquire information regarding a main habitat of pandas and output an answer “pandas are usually dispersed in China”, according to content that is the rare animal documentary. In addition, in the situation of dialog  2  in which the user  10  is watching the zoo-related program, the voice recognition apparatus  100  may acquire information regarding a zoo in which pandas are raised, which is played at a time point where the user  10  is watching, and output an answer “it lives in a Y zoo”, according to content that is the zoo-related program. 
     According to an embodiment, the voice recognition apparatus  100  may output an answer suitable for content being executed. 
       FIG. 18  is a diagram illustrating an example of determining relevance of an answer. 
     Referring to  FIG. 18 , the user  10  asks a question about a character in a situation in which the user  10  is watching a mystery film. In general, when watching a mystery film, the user  10  will not want to watch the mystery film after previously knowing a conclusion. Therefore, when an answer may be a spoiler since the answer contains information on a conclusion, the voice recognition apparatus  100  may interrupt movie watching of the user  10  by outputting the answer. In this case, the voice recognition apparatus  100  may appropriately correct the answer, or determine whether to output the answer after checking an intention of the user  10 . 
     In a situation of dialog  3 , an existing voice recognition apparatus uniformly understands a question of the user  10 , “why did that man come all of a sudden?”, and outputs an answer including information on a conclusion “because he is a criminal” regardless of a genre and a story of movie. 
     Compared with the existing voice recognition apparatus, the voice recognition apparatus  100  according to an embodiment may understand that an answer may be a spoiler since the answer contains information on a conclusion, based on content information in a situation of dialog  4 , and output a relatively appropriate answer by checking the user  10  about whether to output the answer, through “this might be a spoiler question, do you want to hear it?”. 
     According to an embodiment, the voice recognition apparatus  100  may determine relevance of an answer and output a best answer. 
       FIG. 19  is a diagram illustrating an example form of an answer output according to an embodiment. 
     Referring to  FIG. 19 , in a situation in which the user is watching movie, the user  10  asks a question about a name of a vehicle appearing in the movie. The voice recognition apparatus  100  may output an answer of an appropriate form in consideration of content information, context information of the voice recognition apparatus  100 , and the like. For example, when the answer is output by a voice  1910  at a time point where characters in the movie are talking, the output answer overlaps the dialog in the movie, and thus, the user  10  cannot properly hear the answer, and the answer may rather interrupt the movie watching. Therefore, in this case, the answer may be output as subtitles  1920  instead of a voice. 
     According to an embodiment, the voice recognition apparatus  100  may output answer in an appropriate form according to content. 
       FIG. 20  is a diagram illustrating another example wherein the voice recognition apparatus  100  outputs an answer that varies according to context information according to an embodiment. 
     Referring to  FIG. 20 , the voice recognition apparatus  100  may generate a user-adaptive answer using user characteristic information included in the context information  910  of the voice recognition apparatus  100 . 
     For example, when the user  10  is watching a soccer game, the voice recognition apparatus  100  may acquire information regarding a team which the user  10  supports, a preferred voice tone (preference according to gender), a preferred commentator, and the like. When the user  10  supports a team A and prefers a female voice tone, the voice recognition apparatus  100  may find out a commentary biased in favor of the team A and output the commentary with the female voice tone, according to user characteristic information. In addition, when the preferred commentator comments on the soccer game, the voice recognition apparatus  100  may find out and output a commentary of the preferred commentator. 
     According to an embodiment, the voice recognition apparatus  100  may output an answer in an appropriate form according to a user characteristic. 
     The embodiments described above can be written as computer-executable programs and can be implemented in general-use digital computers that execute the programs using a non-transitory computer-readable recording medium. In addition, a structure of data used in the embodiments described above can be recorded in a non-transitory computer-readable recording medium in various ways. In addition, the embodiments described above can be implemented in the form of a recording medium including computer-executable instructions such as computer-executable program modules. For example, methods implemented with a software module or an algorithm may be stored in a non-transitory computer-readable recording medium in the form of computer-readable codes or program instructions executable in a computer system. 
     A non-transitory computer-readable medium may be an arbitrary recording medium which may be accessed by a computer system and includes all types of volatile and nonvolatile media and separated and non-separated media. The non-transitory computer-readable medium may include a magnetic storage medium, e.g., ROM, a floppy disk, a hard disk, or the like, and an optical reading medium, e.g., a storage medium such as compact disk ROM (CD-ROM) or a digital versatile disc (DVD), but is not limited thereto. In addition, the non-transitory computer-readable medium may include a computer storage medium and a communication medium. 
     A plurality of non-transitory computer-readable recording media can also be distributed over a network coupled computer systems, and data stored in the distributed recording media, e.g., program instructions and codes, may be executed by at least one computer system. 
     Specific executions described in the present disclosure are illustrative and do not limit the scope of the present disclosure. For conciseness of the disclosure, disclosure of existing electronic configurations, control systems, software, and other functional aspects of the systems may be omitted. 
     It should be understood that the various example embodiments described herein should be considered in an illustrative sense only and not for purposes of limitation. Descriptions of features or aspects within each embodiment should typically be considered as available for other similar features or aspects in other embodiments. 
     While various example embodiments have been described with reference to the figures, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope as defined, for example, and without limitation, in the following claims.