Patent Publication Number: US-10777193-B2

Title: System and device for selecting speech recognition model

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application is based on and claims priority under 35 U.S.C. § 119 to Korean Patent Application No. 10-2017-0081420, filed on Jun. 27, 2017, in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference herein its entirety. 
     BACKGROUND 
     1. Field 
     The present disclosure generally relates to technology for selecting among various speech recognition models. 
     2. Description of Related Art 
     When utilizing various electronic devices such as a mobile device or a display device, the user may enter a user command by using tools such as a keyboard, or a remote controller. Recently, as the input methods have diversified, electronic devices have included speech recognition capabilities so as to allow voice commands. 
     As such, an electronic device may use one of various speech recognition models for the purpose of converting the user&#39;s speech to text data. Generally, speech recognition models may convert voice to text by analyzing the pronunciation, intonation, etc. of the speech. 
     The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the present disclosure. 
     SUMMARY 
     Generally, in the speech recognition modules described above, a different speech recognition model may be provided for each language. However, the speech recognition model may also be trained by using the intonations of a native speaker. For example, the speech recognition model for recognizing American English may be trained by the intonations of a native American English speaker. 
     However, when the user is not a native speaker, his or her intonations may be different from that of the native speaker. In this case, the electronic device may not precisely recognize the user&#39;s speech. 
     Aspects of the present disclosure are to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below. Accordingly, an aspect of the present disclosure is to provide a method of recognizing the user&#39;s speech using the speech recognition model corresponding to the user&#39;s pronunciation or intonation. 
     In accordance with an aspect of the present disclosure, a system may include at least one network interface, at least one processor operatively connected to the network interface, and at least one memory operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory may further store instructions that, when executed, cause the processor to receive a user&#39;s speech data associated with a plurality of sample texts from an external electronic device displaying a user interface including the plurality of sample texts, where the plurality of sample texts includes words, phrases, and/or sentences, to perform automatic speech recognition (ASR) on the speech data, using the plurality of language models, to select one language model from the plurality of language models based on results from the ASR, and to use the selected one language model as a default language model for speech of the user. 
     In accordance with another aspect of the present disclosure, an electronic device may include a housing, a touchscreen display disposed inside the housing and exposed through a first portion of the housing, a microphone disposed inside the housing and exposed through a second portion of the housing, a wireless communication circuit disposed inside the housing, a processor disposed inside the housing and operatively connected to the touchscreen display, the microphone, and the wireless communication circuit, and a memory disposed inside the housing and operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory further stores instructions that, when executed, cause the processor to display a plurality of sample texts on the touchscreen display, to receive, through the microphone, a plurality of a user&#39;s speeches each associated with one of the plurality of sample texts, to transmit data of the plurality of the user&#39;s speeches to an external server through the wireless communication circuit, to receive results from ASR performed on the data of the plurality of the user&#39;s speeches from the external server through the wireless communication circuit, to select one language model from the plurality of language models based on the received results from the ASR, and to use the selected one language model as a default language model for speech of the user. 
     In accordance with another aspect of the present disclosure, an electronic device may include a housing, a touchscreen display disposed inside the housing and exposed through a first portion of the housing, a microphone disposed inside the housing and exposed through a second portion of the housing, a wireless communication circuit disposed inside the housing, a processor disposed inside the housing and operatively connected to the touchscreen display, the microphone, and the wireless communication circuit, and a memory disposed inside the housing and operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory may further store instructions that, when executed, cause the processor to display a plurality of sample texts on the touchscreen display, to receive, through the microphone, a plurality of a user&#39;s speeches each associated with one of the plurality of sample texts, to transmit data of the plurality of the user&#39;s speeches to an external server through the wireless communication circuit, and to transmit the plurality of language models to the external server. After transmitting the data of the plurality of the user&#39;s speeches and the plurality of language models, the instructions may cause the external server to perform ASR on the data of the plurality of the user&#39;s speeches using the plurality of language models, to select one language model from the plurality of language models based on results from the ASR, and to use the selected one language model as a default language model for speech of the user. 
     In accordance with another aspect of the present disclosure, a system may include at least one network interface, at least one processor operatively connected to the network interface, and at least one memory operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory may further store instructions that, when executed, cause the processor to provide a user interface including a plurality of samples including words, phrases, and/or sentences, to receive a user&#39;s speech data associated with the plurality of samples, to perform ASR on the speech data by using the plurality of language models, to select one language model from the plurality of language models based on the results from the ASR, and to use the selected language model as a default speech recognition model for speech of the user. 
     According to various embodiments of the present disclosure, it is possible to select and use a particular speech recognition language model using a user&#39;s speech, thereby improving the accuracy of speech recognition. 
     Furthermore, according to various embodiments of the present disclosure, the personalized speech recognition language model may be selected by using the user&#39;s speech or the user&#39;s personalization information. 
     In addition, a number of other features directly or indirectly understood through the present disclosure may be provided. 
     Other aspects, advantages, and salient features of the disclosure will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses various embodiments of the present disclosure. 
    
    
     
       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 schematic diagram illustrating an integrated intelligent system, according to an embodiment of the present disclosure; 
         FIG. 2  is a block diagram illustrating a user terminal of an integrated intelligent system, according to an embodiment of the present disclosure; 
         FIG. 3  is a view illustrating an intelligent app of a user terminal while it is executing, according to an embodiment of the present disclosure; 
         FIG. 4A  is a flowchart illustrating operations for determining a speech recognition model, according to an embodiment; 
         FIG. 4B  is a flowchart illustrating operations for determining a speech recognition model, according to an embodiment; 
         FIG. 5  is a flowchart illustrating operations for determining recognition accuracy for various language models, according to an embodiment; 
         FIG. 6  is a flowchart illustrating operations for determining a speech recognition model, according to an embodiment; 
         FIG. 7  is a flowchart illustrating operations for determining recognition accuracy for various language models, according to an embodiment; 
         FIG. 8A ,  FIG. 8B ,  FIG. 8C ,  FIG. 8D ,  FIG. 8E  and  FIG. 8F  are views illustrating screens displayed in a display while a user terminal sets a default speech recognition model, according to an embodiment; 
         FIG. 9A  is a view illustrating a screen displayed while a user terminal waits for reception of a user&#39;s speech; 
         FIG. 9B  is a view illustrating a screen that a user terminal displays when accuracy of ASR is lower than a critical value, according to an embodiment; 
         FIG. 9C  is a view illustrating a screen that a user terminal displays when a standby time for receiving a user&#39;s speech exceeds a specified time period, according to an embodiment; and 
         FIG. 10  is a block diagram of an electronic device in a network environment, according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, various embodiments of the present disclosure will be described with reference to accompanying drawings. However, those of ordinary skill in the art will recognize that various modifications, equivalents, and/or alternatives on the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. 
     Prior to describing an embodiment of the present disclosure, an integrated intelligent system to which an embodiment of the present disclosure may be applied will be described. 
       FIG. 1  is a schematic diagram illustrating an integrated intelligent system, according to an embodiment of the present disclosure. 
     Referring to  FIG. 1 , the integrated intelligent system  10  may include a user terminal  100 , an intelligence server  200 , a personal information server  300 , and/or a suggestion server  400 . 
     The user terminal  100  may provide services for the user through various apps (or an application programs) (e.g., an alarm app, a message app, a picture (gallery) app, or the like) stored in the user terminal  100 . The user terminal  100  may execute and operate an app through an intelligent app (or a speech recognition app) stored in the user terminal  100 . For example, the user may use the speech recognition app and input a voice command “execute alarm app.” Upon receiving the voice command, the speech recognition app may execute the alarm app. The user terminal  100  may also receive a user input for executing an action. For example, the user input may be received through a physical button, a touch pad, a voice input, a remote input, etc. According to an embodiment, the user terminal  100  may be various types of terminal devices or electronic devices connected with Internet, such as mobile phones, smartphones, personal digital assistants (PDA), notebook computers, etc. 
     In other words, the user terminal  100  may receive the user&#39;s speech as user input. The user terminal  100  may receive the user&#39;s speech and may generate a command for operating an app based on the user&#39;s speech. As such, the user terminal  100  may operate the app by using the user&#39;s voice commands. 
     The intelligence server  200  may receive a voice input of the user from the user terminal  100  over a communication network and may convert the voice input to text data. In an embodiment, the intelligence server  200  may generate or select a path rule based on the text data. The path rule may include information about an action or an operation for performing a function of the app specified by the voice input. Also, the path rule may include information about a parameter necessary to perform the action. In addition, the path rule may include the order of the action. The user terminal  100  may receive the path rule, may select the app corresponding to the path rule, and may execute the action included in the path rule in the selected app. 
     Generally, the term “path rule” of the present disclosure may mean, but not limited to, the sequence of states which allows the electronic device to perform the task requested by the user. The path rule may include information about the sequence of the states. For example, the task may be a certain action that the intelligent app is capable of providing, such as the generation of a calendar event, the transmission of a picture to a desired recipient, or the provision of weather information. The user terminal  100  may perform the task by sequentially executing the sequence of states. 
     According to an embodiment, the path rule may be provided or generated by an artificial intelligent (AI) system. The artificial intelligent system may be a rule-based system, or may be a neural network-based system (e.g., a feedforward neural network (FNN) or a recurrent neural network (RNN)). Alternatively, the artificial intelligent system may be a combination of the above-described systems or an artificial intelligent system that is different from the above-described system. According to an embodiment, the path rule may be selected from a set of predefined path rules or may be generated in real time in response to the user request (i.e. the voice input). 
     According to an embodiment, the user terminal  100  may execute the action and may display a screen corresponding to the result of the action in its display. According to another embodiment, the user terminal  100  may execute the action and may not display the result. The user terminal  100  may execute a plurality of actions and may display only the result of some of the actions. For example, the user terminal  100  may display only the result of the last action in the plurality of actions. In another example, the user terminal  100  may not display the results automatically and may display the results only after receiving a user input to display the results. 
     The personal information server  300  may include a database in which user information is stored. For example, the personal information server  300  may receive the user information (e.g., context information, information about execution of apps, etc.) from the user terminal  100  and may store the user information in the database. The intelligence server  200  may receive the user information from the personal information server  300  over the communication network so as to generate the above-described path rules based on the user information. According to an embodiment, the user terminal  100  may receive the user information from the personal information server  300  over the communication network, and may use the user information as information for managing the database. 
     The suggestion server  400  may include a database storing information about functions in the user terminal  100 , information related to the explanation of various functions of an application, and/or functions that may be provided by the user terminal  100 . For example, the suggestion server  400  may include a database associated with functions that the user utilizes by receiving the user information of the user terminal  100  from the personal information server  300 . The user terminal  100  may receive information about the function to be provided from the suggestion server  400  over the communication network and may provide the information to the user. 
       FIG. 2  is a block diagram illustrating a user terminal of an integrated intelligent system, according to an embodiment of the present disclosure. 
     Referring to  FIG. 2 , the user terminal  100  may include an input module  110 , a display  120 , a speaker  130 , a memory  140 , or a processor  150 . The user terminal  100  may further include a housing, and the illustrated components of the user terminal  100  may be housed in the housing. 
     According to an embodiment, the input module  110  may receive user inputs from the user. For example, the input module  110  may receive user inputs from connected external devices such as a keyboard or a headset. In another example, the input module  110  may include a touch panel coupled to the display  120 , which renders the display  120  to be a touchscreen. In yet another example, the input module  110  may include one or more hardware or physical keys exposed through the housing of the user terminal  100 . 
     According to an embodiment, the input module  110  may include a microphone that is capable of receiving the speech of the user as voice signals. For example, the input module  110  may include a speech input system and may receive the speech of the user as voice signals through the speech input system. 
     According to an embodiment, the display  120  may display images, videos, and/or execution screens of applications of the user terminal. For example, the display  120  may display graphic user interfaces (GUIs) of apps of the user terminal. 
     According to an embodiment, the speaker  130  may output voice signals. For example, the speaker  130  may output voice signals generated by the user terminal  100 . 
     According to an embodiment, the memory  140  may store a plurality of apps (or application programs)  141  and  143 . The plurality of apps  141  and  143  stored in the memory  140  may be selected and executed based on user input. 
     According to an embodiment, the memory  140  may include a database storing information necessary to recognize the user input. In addition, the memory  140  may include a log database storing log information and a persona database capable of storing user information. 
     According to an embodiment, the plurality of apps  141  and  143  stored in the memory  140  may be loaded by an execution manager module  153  of the processor  150  so that the apps can be executed. The plurality of apps  141  and  143  may include execution service modules  141   a  and  143   a  which can perform various functions. For example, the plurality of apps  141  and  143  may perform a plurality of actions (e.g., a sequence of states)  141   b  and  143   b  through execution service modules  141   a  and  143   a  for the purpose of performing functions. That is, the execution service modules  141   a  and  143   a  may be activated by the execution manager module  153 , and then may execute the plurality of actions  141   b  and  143   b.    
     According to an embodiment, when the actions  141   b  and  143   b  of the apps  141  and  143  are executed, execution state screens according to the execution of the actions  141   b  and  143   b  may be displayed in the display  120 . For example, the execution state screens may be screens displaying the results of the actions  141   b  and  143   b  when the actions are completed. In another example, the execution state screens may be screens prompting the user to enter additional input for the completion of the actions  141   b  and  143   b.    
     According to an embodiment, the execution service modules  141   a  and  143   a  may execute the actions  141   b  and  143   b  depending on the above-described path rules. For example, the execution service modules  141   a  and  143   a  may be activated by the execution manager module  153 , may receive an execution request from the execution manager module  153  that is based on a path rule, and may execute functions of the apps  141  and  143  by performing the actions  141   b  and  143   b  based on the execution request. When the execution of the actions  141   b  and  143   b  is completed, the execution service modules  141   a  and  143   a  may transmit the results to the execution manager module  153 . 
     According to an embodiment, the plurality of the actions  141   b  and  143   b  may be sequentially executed. When the execution of one action (e.g., action  1  of the first app  141  or action  1  of the second app  143 ) is completed, the execution service modules  141   a  and  143   a  may open the next action (e.g., action  2  of the first app  141  or action  2  of the second app  143 ) and may transmit the results of the first action to the execution manager module  153 . Here, “opening” an action means changing the state of the action to an executable state or to prepare the execution of the action. In other words, when the action is not opened, it may be not executed. When the results of the first action are received, the execution manager module  153  may transmit the execution request associated with the next action (e.g., action  2  of the first app  141  or action  2  of the second app  143 ) to the execution service modules  141   a  and  143   a . According to an embodiment, the plurality of apps  141  and  143  may be sequentially executed. For example, after receiving the results of the last action (e.g., action  3  of the first app  141 ) of the first app  141 , the execution manager module  153  may transmit the execution request of the first action (e.g., action  1  of the second app  143 ) of the second app  143  to the execution service module  143   a.    
     According to an embodiment, when the plurality of the actions  141   b  and  143   b  are executed in the apps  141  and  143 , result screens from the execution of the actions  141   b  and  143   b  may be displayed in the display  120 . Alternatively, not all of the result screens may be displayed in the display  120 . 
     According to an embodiment, the memory  140  may store an intelligent app (e.g., a speech recognition app) operating in conjunction with an intelligence agent  151  included in the processor  150 . The intelligent app may receive and process the speech of the user as voice signals. According to an embodiment, the intelligent app may be initiated or prompted by a specific input (e.g., an input through a hardware key, an input through a touchscreen, or a specific voice input) input through the input module  110 . 
     According to an embodiment, the processor  150  may control overall actions of the user terminal  100 . For example, the processor  150  may control the input module  110  to receive user input. The processor  150  may control the display  120  to display content. The processor  150  may control the speaker  130  to output sounds or voice signals. The processor  150  may control the memory  140  to read or store information. The processor  150  may include a microprocessor or any suitable type of processing circuitry, such as one or more general-purpose processors (e.g., ARM-based processors), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a Graphical Processing Unit (GPU), a video card controller, etc. In addition, it would be recognized that when a general purpose computer accesses code for implementing the processing shown herein, the execution of the code transforms the general purpose computer into a special purpose computer for executing the processing shown herein. Certain of the functions and steps provided in the Figures may be implemented in hardware, software or a combination of both and may be performed in whole or in part within the programmed instructions of a computer. No claim element herein is to be construed under the provisions of 35 U.S.C. 112(f), unless the element is expressly recited using the phrase “means for.” In addition, an artisan understands and appreciates that a “processor” or “microprocessor” may be hardware in the claimed disclosure. Under the broadest reasonable interpretation, the appended claims are statutory subject matter in compliance with 35 U.S.C. § 101. 
     According to an embodiment, the processor  150  may include the intelligence agent  151 , the execution manager module  153 , and/or an intelligent service module  155 . In an embodiment, the processor  150  may drive the intelligence agent  151 , the execution manager module  153 , and/or the intelligence service module  155  by executing instructions stored in the memory  140 . Modules described in various embodiments of the present disclosure may be implemented by hardware and/or by software. In various embodiments of the present disclosure, it is understood that actions executed by the intelligence agent  151 , the execution manager module  153 , and/or the intelligence service module  155  are actions executed by the processor  150 . 
     According to an embodiment, the intelligence agent  151  may generate a command for operating an app (e.g. the app  141 ) based on voice signal received as user input. According to an embodiment, the execution manager module  153  may receive the generated command from the intelligence agent  151 , and may select and execute the app  141 . According to an embodiment, the intelligence service module  155  may manage information of the user and may use the information of the user to process the user input. 
     The intelligence agent  151  may transmit and process the voice signal received through the input module  110  to the intelligence server  200 . 
     According to an embodiment, before transmitting the voice signal to the intelligence server  200 , the intelligence agent  151  may pre-process the user input. According to an embodiment, to pre-process the user input, the intelligence agent  151  may include an adaptive echo canceller (AEC) module, a noise suppression (NS) module, an end-point detection (EPD) module, and/or an automatic gain control (AGC) module. The AEC may remove unwanted echoes in the voice signal. The NS module may suppress background noise in the voice signal. The EPD module may detect the end-point of the user speech in the voice signal so that the intelligence server  200  can readily determine the part of the voice signal that contains the user&#39;s speech. The AGC module may adjust the volume of the voice signal so that the voice signal can be properly processed by the intelligence server  200 . According to an embodiment, the intelligence agent  151  may include execute all of the pre-processing above for maximum performance. However, in another embodiment, the intelligence agent  151  may execute only some of the pre-processing to save battery power. 
     According to an embodiment, the intelligence agent  151  may include a wakeup recognition module recognizing a specific user voice input to wake up the user terminal. The wakeup recognition module may recognize the wake up command using the speech recognition module. When the wakeup recognition module receives the wake up command, the wakeup recognition module may activate the intelligence agent  151  to receive the user input. According to an embodiment, the wakeup recognition module of the intelligence agent  151  may be implemented with a low-power processor, which may include an audio codec. According to an embodiment, the intelligence agent  151  may be activated depending on the user input entered through a hardware key. When the intelligence agent  151  is activated, an intelligent app (e.g., a speech recognition app) operating in conjunction with the intelligence agent  151  may be executed. 
     According to an embodiment, the intelligence agent  151  may include a speech recognition module for executing the user input. The speech recognition module may recognize the user input for executing an action in an app. For example, the speech recognition module may recognize a limited number of voice inputs for executing an action in the apps  141  and  143  (e.g., speech such as “click” for executing a capturing action in a camera app). The speech recognition module may be used in conjunction with the intelligence server  200  to assist the intelligence server  200 . For example, the speech recognition module may recognize its limited number of voice inputs more quickly than the intelligence server  200 . According to an embodiment, the speech recognition module may be implemented in a secondary processor separate from the processor  150 . 
     According to an embodiment, the speech recognition module and/or the wakeup recognition module of the intelligence agent  151  may recognize the user input by using an algorithm for recognizing user voice. For example, the algorithm may be a hidden Markov model (HMM) algorithm, an artificial neural network (ANN) algorithm, a dynamic time warping (DTW) algorithm, etc. 
     According to an embodiment, the intelligence agent  151  may convert the voice input of the user to text data. Alternatively, the intelligence agent  151  may transmit the voice of the user to the intelligence server  200 . The intelligence server  200  in turn converts the voice to text, and may transmit the text to the intelligence agent  151 . In either case, the intelligence agent  151  may display the text data in the display  120 . 
     According to an embodiment, the intelligence agent  151  may receive path rules from the intelligence server  200 . According to an embodiment, the intelligence agent  151  may transmit the path rules to the execution manager module  153 . 
     According to an embodiment, the intelligence agent  151  may transmit the execution result log that resulted from the path rule to the intelligent service module  155 , and the transmitted execution result log may be accumulated and managed as preference information of the user of the persona module  155   b.    
     According to an embodiment, the execution manager module  153  may receive the path rule from the intelligence agent  151  to execute the apps  141  and  143  and may prompt the apps  141  and  143  to execute the actions  141   b  and  143   b  based on the path rule. For example, the execution manager module  153  may transmit command information (e.g., information about the path rule) for executing the actions  141   b  and  143   b  to the apps  141  and  143  and may receive results of the actions  141   b  and  143   b  from the apps  141  and  143 . 
     According to an embodiment, the execution manager module  153  may mediate the command information (e.g., information about the path rule) for executing the actions  141   b  and  143   b  between the intelligence agent  151  and the apps  141  and  143 . The execution manager module  153  may bind the apps  141  and  143  to be executed based on the path rule and may transmit the command information (e.g., information about the path rule) to the apps  141  and  143 . For example, the execution manager module  153  may sequentially transmit commands for the actions  141   b  and  143   b  included in the path rule to the apps  141  and  143  and may sequentially execute the actions  141   b  and  143   b  of the apps  141  and  143  based on the path rule. 
     According to an embodiment, the execution manager module  153  may manage the execution states of the actions  141   b  and  143   b . For example, the execution manager module  153  may receive information about the execution states of the actions  141   b  and  143   b  from the apps  141  and  143 . When the actions  141   b  and  143   b  are partially executed and require additional user input, the execution manager module  153  may transmit information about the partial execution to the intelligence agent  151 . The intelligence agent  151  may then cause the display  120  to display an interface so that the user can input additional information, such as additional parameter information. In another example, when the actions  141   b  and  143   b  are executed, speech may be received from the user, and the execution manager module  153  may transmit information about the execution states of the apps  141  and  143  to the intelligence agent  151 . The intelligence agent  151  may derive parameter information of the speech of the user using the intelligence server  200  and may transmit the received parameter information to the execution manager module  153 . The execution manager module  153  may then use the parameter information in the execution of the actions  141   b  and  143   b.    
     According to an embodiment, the execution manager module  153  may transmit parameter information included in the path rule to the apps  141  and  143 . When the plurality of apps  141  and  143  are sequentially executed, the execution manager module  153  may transmit parameter information from one app to another app. 
     According to an embodiment, the execution manager module  153  may receive a plurality of path rules. The execution manager module  153  may select the plurality of path rules based on user speech. For example, when the user&#39;s speech specifies app  141  executing action  141   a  but does not specify app  143  executing action  143   b , the execution manager module  153  may receive a plurality of path rules, in which the same app  141  (e.g., gallery app) executing the one action  141   a  is executed and in which a different app  143  (e.g., message app or Telegram app) executing the other action  143   b  is executed. For example, the execution manager module  153  may execute the same actions  141   b  and  143   b  (e.g., the same successive actions  141   b  and  143   b ) of the plurality of path rules. When the execution manager module  153  executes an action, the execution manager module  153  may display a state screen allowing the user to select either apps  141  or  143  for completing the action. 
     According to an embodiment, the intelligence service module  155  may include a context module  155   a , a persona module  155   b , and/or a suggestion module  155   c.    
     The context module  155   a  may collect current states of the apps  141  and  143  from the apps  141  and  143 . For example, the context module  155   a  may receive context information indicating the current states of the apps  141  and  143 . 
     The persona module  155   b  may manage personal information of the user of the user terminal  100 . For example, the persona module  155   b  may collect the usage information and execution results of the user terminal  100 . 
     The suggestion module  155   c  may predict the intent of the user in order to recommend functions of the user terminal to the user. For example, the suggestion module  155   c  may recommend functions based on the current state (e.g., time, place, situation, current execution state of the user terminal, etc.) of the user terminal. 
       FIG. 3  is a view illustrating an intelligent app of a user terminal while it is executing, according to an embodiment of the present disclosure. 
       FIG. 3  illustrates that the user terminal  100  receives a user input to execute the intelligent app (e.g., a speech recognition app) operating in conjunction with the intelligence agent  151 . 
     According to an embodiment, the user terminal  100  may begin to execute the intelligent app when the user enters an input through a hardware key  112 . For example, when the user terminal  100  receives the user input through the hardware key  112 , the user terminal  100  may display a UI  121  of the intelligent app in the display  120 . Subsequently, the user may touch a speech recognition button  121   a  in the UI  121  to enter a voice input  116   b . In another example, the user may enter voice input  116   b  while continuously pressing the hardware key  112 . 
     According to an embodiment, the user terminal  100  may execute the intelligent app using a microphone  111 . For example, when a specified voice input  116   a  (e.g., wake up!) is entered through the microphone  111 , the user terminal  100  may display the UI  121 . 
     Hereinafter, it is assumed that a system including the user terminal  100  and the intelligence server  200  performs the process of  FIGS. 4A to 7 . 
     In addition, in descriptions of  FIGS. 4A to 7 , it is understood that the operations described as being performed by the user terminal  100  is controlled by the processor  150  of the user terminal  100 . The user terminal  100  may further include a communication circuit  160 , and the user terminal  100  may communicate with the intelligence server  200  through the wireless communication circuit  160 . 
     The operations described as being performed by the user terminal  100  may be implemented with instructions capable of being performed (or executed) by the processor  150  of the user terminal  100 . For example, the instructions may be stored in a computer-readable recording medium or the memory  140  of the user terminal  100  illustrated in  FIG. 2 . 
     The intelligence server  200  may include at least one network interface  210 , at least one processor  220 , and at least one memory  230 , as shown in  FIG. 2 . 
     The network interface  210  may support the establishment of a communication channel with the user terminal  100  and may the execution wired or wireless communication through the established communication channel. 
     The processor  220  may be operatively connected to the network interface  210 . In descriptions of  FIGS. 4A to 7 , the processor  220  may control the operations described as being performed by the intelligence server  200 . The operations described as being performed by the intelligence server  200  may be implemented with instructions capable of being performed (or executed) by the processor  220  of the intelligence server  200 . For example, the instructions may be stored in a computer-readable recording medium or the memory  230  of the intelligence server  200 . The memory  230  may be operatively connected to the processor  220 . 
     In an embodiment, the memory  140  of the user terminal  100  and the memory  230  of the intelligence server  200  may store a plurality of language models of a first language. The plurality of language models of the first language may have different speech models from one another, and different language models may be provided for users whose native language is not the first language. For example, the first language may be English, and the native language that is different from the first language may be Korean, Chinese, Japanese, Hindi, French, British English, German, Spanish, Portuguese, Russian, etc. 
     For example, the plurality of language models may include a Korean English speech model, a Chinese English speech model, a Japanese English speech model, a Hindi English speech model, and/or a Spanish English speech model. The Korean English speech model is a language model for recognizing English spoken by speakers whose native or mother language is Korean, i.e. native Korean speakers; the Chinese English speech model is a language model for recognizing English spoken by native Chinese speakers; the Japanese English speech model is a language model for recognizing English spoken by native Japanese speakers, etc. 
     On the other hand, according to another embodiment, the first language described above may not be English but instead be another language such as Korean, Chinese, Japanese, etc. 
     According to an embodiment, the memory  140  of the user terminal  100  and the memory  230  of the intelligence server  200  may store a plurality of sample texts including words, phrases, and/or sentences. 
     According to an embodiment, the memory  140  of the user terminal  100  and the memory  230  of the intelligence server  200  may include a plurality of text groups respectively corresponding to the plurality of language models, and each of text groups may include one or more sample texts. 
     According to an embodiment, the memory  140  of the user terminal  100  and the memory  230  of the intelligence server  200  may include one or more sentences corresponding to the Korean English speech model, one or more sentences corresponding to the Chinese English speech model, one or more sentences corresponding to the Japanese English speech model, etc. 
     According to an embodiment, sample texts included in the text groups may include keywords that persons corresponding to the respective text groups distinctively pronounce. 
     Table 1 includes exemplary keywords that persons distinctively pronounce, and sample texts including the keyword. 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Language 
                 Key Word 
                 Speech 
               
               
                   
               
             
            
               
                 Chinese 
                 Usually 
                 I usually take a walk in morning 
               
               
                 Korean 
                 bookmark 
                 Search for Samsung in my Internet bookmarks 
               
               
                 Hindi 
                 Euler 
                 Tell me about Euler&#39;s formula 
               
               
                 Spain 
                 bridge 
                 A long time ago, there was a bridge here 
               
               
                   
               
            
           
         
       
     
     According to an embodiment, pronunciation of “usually” by native Chinese speakers, pronunciation of “bookmark” by native Korean speakers, pronunciation of “Euler” by native Hindi speakers, and pronunciation of “bridge” by native Spanish speakers may be distinguished from the pronunciations of these words by native English speakers. 
     According to an embodiment, when a keyword that a person distinctively pronounces and a sample text including the corresponding keyword are used, the characteristic pronunciation of the person whose native language is not English may be determined. 
     According to an embodiment, the memory  140  of the user terminal  100  and the memory  230  of the intelligence server  200  may include information about the pre-specified keywords described above. 
     According to an embodiment, the memory  140  of the user terminal  100  may store personalization information of the user. The personalization information of the user may include the type (e.g., Korean character, Chinese character, Japanese character, etc.) of characters used in contacts stored in the memory  140 , the names stored in the contacts, local settings, language setting information of a virtual keyboard of the user terminal, etc. 
       FIG. 4A  is a flowchart illustrating operations for determining a speech recognition model, according to an embodiment. 
     In operation  401 , the processor  150  of the user terminal  100  may display a plurality of sample texts on the touchscreen display  120 . 
     According to an embodiment, the plurality of sample texts may include words, phrases and/or sentences. According to an embodiment, the processor  150  of the user terminal  100  may provide the plurality of sample texts as a user interface on the touchscreen display  120 . 
     According to an embodiment, the processor  150  of the user terminal  100  may determine the language of the displayed sample texts based on language setting information. For example, when the language of the user terminal  100  is set to English, the processor  150  of the user terminal  100  may display a plurality of sample texts written in English. For another example, when the language of the user terminal  100  is set to Korean, the processor  150  of the user terminal  100  may display a plurality of sample texts written in Korean. 
     According to an embodiment, the processor  150  of the user terminal  100  may select the plurality of sample texts from a plurality of text groups stored in the memory  140  and may display the selected plurality of sample texts on the touchscreen display  120 . 
     For example, the memory  140  may include a text group corresponding to each of a first language model, a second language model, and a third language model. The processor  150  of the user terminal  100  may select a sample text from the text group corresponding to the first language model, a sample text from the text group corresponding to the second language model, and a sample text from the text group corresponding to the third language model. In other words, the selected plurality of sample texts may include at least one sample corresponding to each of the language models stored in the memory  140 . 
     According to an embodiment, the processor  150  of the user terminal  100  and the processor  220  of the intelligence server  200  may use a preset plurality of sample texts. According to an embodiment, the processor  150  of the user terminal  100  may display the preset plurality of sample texts on the touchscreen display  120 . 
     According to an embodiment, before operation  401 , the processor  150  of the user terminal  100  may receive the plurality of sample texts from the intelligence server  200  through the communication circuit  160  and may display the received plurality of sample texts on the touchscreen display  120 . 
     According to an embodiment, before operation  403 , the processor  150  of the user terminal  100  may display a first object on the touchscreen display  120  for activating the reception of a user&#39;s speech. According to an embodiment, when the user selects the first object through the touchscreen display  120 , the processor  150  of the user terminal  100  may receive the user&#39;s speech in operation  403  described below. 
     According to an embodiment, before operation  403 , the processor  150  of the user terminal  100  may display a second object on the touchscreen display  120  for using a preset language model as a default speech recognition model. According to an embodiment, when the user selects the second object through the touchscreen display  120 , the processor  150  of the user terminal  100  may allow the intelligence server  200  to use the preset language model as the default speech recognition model. 
     In operation  403 , the user terminal  100  and the intelligence server  200  may determine recognition accuracy for each language model (e.g. an English language model for native Korean speakers, an English language model for native Chinese speakers, etc.). 
     According to an embodiment, in operation  403 , the processor  150  of the user terminal  100  may receive the user&#39;s speech associated with the displayed plurality of sample texts through the microphone  111 . The processor  150  of the user terminal  100  then transmits data associated with the received user&#39;s speech to the intelligence server  200  through the communication circuit  160 . Next, the processor  220  of the intelligence server  200  performs automatic speech recognition (ASR) on data associated with the user&#39;s speech by using a plurality of language models. Finally, the processor  220  of the intelligence server  200  determines the recognition accuracy for each language model based on the results of the ASR. 
     According to an embodiment, when the processor  150  of the user terminal  100  transmits the data associated with the user&#39;s speech to the intelligence server  200 , the processor  150  of the user terminal  100  may also transmit data associated with the sample texts to the intelligence server  200 . For example, the processor  150  of the user terminal  100  may transmit information regarding the sorting order of the sample texts to the intelligence server  200 . 
     According to an embodiment, the processor  150  of the user terminal  100  may perform the ASR instead of the processor  220  of the intelligence server  200 . When this is the case, the processor  150  of the user terminal  100  may use various recognition schemes such as isolated word recognition, connected word recognition, large vocabulary recognition, etc. 
     According to an embodiment, operation  403  may further include an operation in which the processor  220  of the intelligence server  200  determines scores on the results of the ASR. Detailed descriptions of how the scores are determined is provided below and in connection with  FIGS. 5 and 7 . 
     According to an embodiment, the processor  220  of the intelligence server  200  may determine the score for each word or phoneme included in the sample text. According to an embodiment, the processor  220  of the intelligence server  200  may determine the score on some pre-specified keyword. For example, when the sample text is “choose a music genre for me” and the pre-specified keyword is “genre”, the processor  220  of the intelligence server  200  may perform ASR on “choose a music genre for me” and may determine the score on “genre.” According to an embodiment, the processor  220  of the intelligence server  200  may determine the score on each of words (e.g., “choose,” “a music,” “genre,” “for,” and “me”) in the sample text and may determine the final score on the sample text by summing scores or calculating an average of the scores. 
     According to an embodiment, when the processor  220  of the intelligence server  200  determines recognition accuracy for each language model, the processor  220  of the intelligence server  200  may determine recognition accuracy by using the determined scores. 
     For example, the processor  220  of the intelligence server  200  may determine, based on the ASR, a score for each language model for each piece of data in the user&#39;s speech. For example, when there are three pieces of data in the user&#39;s speech and there are four language models, the processor  220  of the intelligence server  200  may determine twelve scores. In an embodiment, the processor  220  of the intelligence server  200  may determine the recognition accuracy for each language model by summing scores for each language model or calculating an average for each language model. For example, the processor  220  of the intelligence server  200  may determine three scores based on data associated with the user&#39;s first speech, data associated with the user&#39;s second speech, and data associated with the user&#39;s third speech using the first language model. The processor  220  may then determine the recognition accuracy of the first language model by summing three scores or calculating the average. 
     For another example, the processor  220  of the intelligence server  200  may determine the recognition accuracy for each language model by using the highest score among the scores. 
     According to an embodiment, the processor  220  of the intelligence server  200  may apply weights based on the sample text and/or the language model corresponding to each score, to the scores to determine the recognition accuracy for each language model. 
     According to an embodiment, since the scores are determined based on the results of the ASR, the scores may be dependent on the sample text, the user&#39;s speech, and the language model. Thus, according to an embodiment, the weights applied to the scores may be based on the sample text and/or the language model that affects the scores. 
     For example, the processor  220  of the intelligence server  200  may perform ASR on the user&#39;s first speech associated with the first sample text and the user&#39;s second speech associated with the second sample text using a first language model and a second language model. In this example, the score on the user&#39;s first speech by using the first language model may be 2 points; the score on the user&#39;s first speech by using the second language model may be 2.5 points; the score on the user&#39;s second speech by using the first language model may be 3 points; and the score on the user&#39;s second speech by using the second language model may be 4 points. 
     As an example, the weight based on the first sample text and the first language model may be 0.2; the weight based on the first sample text and the second language model may be 0.2; the weight based on the second sample text and the first language model may be 0.3; and the weight based on the second sample text and the second language model may be 0.1. 
     In an embodiment, in the case where the weight is not applied, the recognition accuracy of the first language model may be 5(=2+3) and the recognition accuracy of the second language model may be 6.5(=2.5+4). In an embodiment, in the case where the weight is applied, the recognition accuracy of the first language model may be 1.3(=2×0.2+3×0.3) and the recognition accuracy of the second language model may be 0.9(=2.5×0.2+4×0.1). Thus, the recognition accuracy for each language model may be changed by applying the weight. 
     According to an embodiment, the processor  220  of the intelligence server  200  may determine the recognition accuracy for each language model by applying confidence levels corresponding to the ASR to the scores. 
     According to an embodiment, the processor  220  of the intelligence server  200  may determine the confidence levels by comparing the texts obtained by performing the ASR to the sample texts. 
     According to an embodiment, the confidence levels may indicate how reliable the result of ASR is. For example, the confidence levels may be expressed as values between 0 and 1 or may be expressed as values between 0 and 100. 
     In an embodiment, the score on the user&#39;s first speech by using the first language model may be 2 points; the score on the user&#39;s first speech by using the second language model may be 2.5 points; the score on the user&#39;s second speech by using the first language model may be 3 points; and the score on the user&#39;s second speech by using the second language model may be 4 points. In an embodiment, the confidence level of the first ASR result (the ASR of the user&#39;s first speech using the first language model) may be determined as 0.7; the confidence level of the second ASR result (the ASR of the user&#39;s first speech using the second language model) may be determined as 0.8; the confidence level of the third ASR result (the ASR of the user&#39;s second speech using the first language model) may be determined as 0.5; and the confidence level of the fourth ASR result (the ASR of the user&#39;s second speech using the second language model) may be determined as 0.4. 
     In an embodiment, the processor  220  of the intelligence server  200  may apply the confidence levels to the scores on each ASR result and may determine that the recognition accuracy of the first language model is 3.4(=2×0.7+2.5×0.8) and the recognition accuracy of the second language model is 3.1(=3×0.5+4×0.4). 
     Thus, according to an embodiment, the processor  220  of the intelligence server  200  may determine the recognition accuracy by using the determined confidence levels. 
     According to an embodiment, the processor  150  of the user terminal  100  may transmit personalization information of the user to the intelligence server  200  through the communication circuit  160 . The processor  220  of the intelligence server  200  may apply weights based on the personalization information to the scores to determine the recognition accuracy for each language model. For example, in the case where the language used in the virtual keyboard of the user terminal  100  is Korean, the processor  150  of the user terminal  100  may assign a higher weight to the language model associated with Korean. According to an embodiment, when weights are not applied, the recognition accuracies of two language models may be the same as each other. But when the higher weight is assigned to the Korean language model, the recognition accuracy of the Korean language model may be higher. 
     According to an embodiment, operations that are part of operation  403  may be performed sequentially and repeatedly. For example, operations that are part of operation  403  may include the operation where the processor  150  of the user terminal  100  receives the user&#39;s first speech, which is associated with the first sample text, the operation where the processor  150  of the user terminal  100  transmits data associated with the user&#39;s first speech to the intelligence server  200  through the communication circuit  160 , the operation where the processor  220  of the intelligence server  200  performs ASR on the data associated with the user&#39;s first speech using a plurality of language models, and the operation where the processor  220  of the intelligence server  200  determines the recognition accuracy for each language model based on the results of the ASR. These operations may be performed repeatedly. The detailed descriptions thereof will be described with reference to  FIG. 5 . 
     In operation  405 , the processor  220  of the intelligence server  200  may select one of language models based on the results from the ASR. 
     According to an embodiment, the processor  220  of the intelligence server  200  may use the recognition accuracy as indicated by the scored to select one of language models. For example, the processor  220  of the intelligence server  200  may select the language model having the highest recognition accuracy. 
     According to an embodiment, the processor  220  of the intelligence server  200  may receive the personalization information of the user from the user terminal  100  through the network interface  210 . According to an embodiment, the personalization information of the user may include the type of characters used in contacts stored in the memory  140  (e.g. Korean, Chinese, etc.), the names stored in the contacts, local settings, or language setting information of the virtual keyboard of the user terminal. 
     According to an embodiment, the processor  220  of the intelligence server  200  may select one of language models based on the received personalization information and the results from the performed ASR. 
     For example, the processor  220  of the intelligence server  200  may select one or more first language models based on the personalization information of the user and may select a second language model based on the results from the ASR. When the second language model is included in the one or more first language models, the processor  220  of the intelligence server  200  may select the second language model as a default automatic language model. 
     In operation  407 , the processor  220  of the intelligence server  200  may use the selected language model as the default speech recognition model. 
     According to an embodiment, the processor  220  of the intelligence server  200  may use the selected language model for the purpose of obtaining voice data from the user. 
     According to an embodiment, the processor  220  of the intelligence server  200  may transmit a response indicating that the setting of the default speech recognition model is completed, through the network interface  210 . The processor  150  of the user terminal  100  may then display a message indicating that settings of the default speech recognition model is completed on the touchscreen display  120 . 
       FIG. 4B  is a flowchart illustrating operations for determining a speech recognition model, according to an embodiment. 
     The operations of  FIG. 4B  may correspond to operation  403  to operation  405  of  FIG. 4A . 
     In operation  451 , the processor  220  of the intelligence server  200  may receive various pieces of data associated with a plurality of user&#39;s speeches from a user terminal. 
     In operation  453 , the processor  220  of the intelligence server  200  may perform ASR on the data for each language model. 
     According to an embodiment, the processor  220  of the intelligence server  200  may perform ASR on the data for each language model by using a plurality of language models. According to an embodiment, the processor  220  of the intelligence server  200  may use various ASR schemes as described above. 
     In operation  455 , the processor  220  of the intelligence server  200  may calculate scores on the ASR results for each language model. 
     According to an embodiment, the processor  220  of the intelligence server  200  may calculate scores for pre-specified keywords in the user&#39;s speech as well as calculating scores on the entire user&#39;s speech. 
     In operation  457 , the processor  220  of the intelligence server  200  may sum scores for each language model. 
     According to an embodiment, when data of four user&#39;s speeches are received, there may be four scores for each language model. The processor  220  of the intelligence server  200  may sum the four scores for each language model. 
     In operation  459 , the processor  220  of the intelligence server  200  may compare the summed scores. 
     According to an embodiment, the processor  220  of the intelligence server  200  may compare the highest score among the scores for each language model. 
     In operation  461 , the processor  220  of the intelligence server  200  may select the language model having the highest score. 
     According to an embodiment, the processor  220  of the intelligence server  200  may select a language model, the summed score of which is the highest, as a language model used as the default automatic language model. 
     According to another embodiment, operation  455  to operation  461  may be performed by the processor  150  of the user terminal  100 . For the purpose of performing operation  455  to operation  461 , before operation  455 , the processor  150  of the user terminal  100  may receive the ASR results for each language model from the intelligence server  200 . 
       FIG. 5  is a flowchart illustrating operations for determining recognition accuracy for various language models, according to an embodiment. 
     According to an embodiment, operations illustrated in  FIG. 5  may correspond to the operation  403  of  FIG. 4A . According to an embodiment, embodiments capable of being applied to operation  403  may be also applied to operation  501  to operation  561 . 
     In operation  501 , the processor  150  of the user terminal  100  may receive data associated with the user&#39;s first speech through the microphone  111 . The speech may be associated with a first sample text, and the first sample text may be one of a plurality of sample texts. 
     In operation  503 , the processor  150  of the user terminal  100  may transmit data of the user&#39;s first speech to the intelligence server  200  through the communication circuit  160 . In other words, in operation  551 , the processor  220  of the intelligence server  200  may receive the data of the user&#39;s first speech through the network interface  210 . 
     In operation  553 , the processor  220  of the intelligence server  200  may perform ASR on the data of the user&#39;s first speech by using a plurality of language models. 
     According to an embodiment, the processor  220  of the intelligence server  200  may determine the accuracy of the ASR. According to an embodiment, the processor  220  of the intelligence server  200  may determine an error rate based on the first sample text and the user&#39;s first speech. According to an embodiment, the processor  220  of the intelligence server  200  may determine a confidence level by comparing the first sample text and the text resulting from the ASR. According to an embodiment, the processor  220  of the intelligence server  200  may determine accuracy based on at least one of the determined error rate and the confidence level. In an embodiment, the determined error rate has an inverse relationship with the accuracy. If the determined error rate is lower, the accuracy of the ASR may be higher and if the determined confidence level is higher, the accuracy of the ASR may be higher. 
     As described above, the processor  220  of the intelligence server  200  may determine the error rate of the ASR based on the first sample text and the user&#39;s first speech. For example, the processor  220  of the intelligence server  200  may compare the text obtained from the ASR with the first sample text to determine the error rate of the ASR. 
     In an embodiment, in the case where the processor  220  of the intelligence server  200  performs the ASR on the data of the user&#39;s first speech by using five language models, the processor  220  of the intelligence server  200  may determine five respective error rates. According to an embodiment, the processor  220  of the intelligence server  200  may determine that the lowest error rate to be the error rate of the ASR. According to another embodiment, the processor  220  of the intelligence server  200  may determine that the average of the five error rates is the error rate of the ASR. However, the method of determining the error rate of the ASR is not limited to the above-described examples. 
     According to an embodiment, the processor  220  of the intelligence server  200  may determine the confidence level based on the text that resulted from the ASR. 
     According to an embodiment, the confidence level may indicate how reliable the ASR is. For example, the confidence level may be expressed as a value between 0 and 1 or may be expressed as a value between 0 and 100. 
     In the case where the determined accuracy is less than a critical value in operation  555 , in operation  557 , the processor  220  of the intelligence server  200  may transmit a first response to the user terminal  100  through the network interface  210 . In the case where the determined accuracy is equal to or greater than the critical value in operation  555 , in operation  559 , the processor  220  of the intelligence server  200  may transmit a second response, which is different from the first response, to the user terminal  100  through the network interface  210 . In other words, in operation  505 , the processor  150  of the user terminal  100  may receive the first response or the second response through the communication circuit  160 . 
     In the case where the processor  150  of the user terminal  100  receives the first response in operation  507 , in operation  501 , the processor  150  of the user terminal  100  may prompt the user to utter the first speech again. In turn, the processor  150  of the user terminal  100  may re-receive the user&#39;s first speech through the microphone  111 . 
     In operation  561 , the processor  220  of the intelligence server  200  may determine the recognition accuracy for each language model associated with the data of the user&#39;s first speech. 
     In the case where the processor  150  of the user terminal  100  receives the second response (i.e. when the first response is not received in operation  507 ), in operation  509 , the processor  150  of the user terminal  100  may receive the user&#39;s second speech associated with the second sample text, which is different from a first sample text. 
     According to an embodiment, when the processor  150  of the user terminal  100  receives the second response, the processor  150  of the user terminal  100  may display an indicator (e.g., check mark) at the location of the touchscreen display  120  corresponding to the first sample text. 
     According to an embodiment, assuming that the user&#39;s second speech is received by the processor  150  at operation  501 , the above-described operation  501  to operation  561  may be repeatedly performed until the all the plurality of sample texts displayed on the touchscreen display  120  of the user terminal  100  are processed. 
       FIG. 6  is a flowchart illustrating operations for determining a speech recognition model, according to an embodiment. 
     According to an embodiment, operation  601  to operation  607  of  FIG. 6  may correspond to operation  401  to operation  407  of  FIG. 4A . According to an embodiment, embodiments capable of being applied to operation  401  to operation  407  may be also applied to operation  601  to operation  607 . 
     In operation  601 , the processor  150  of the user terminal  100  may display a plurality of sample texts on the touchscreen display  120 . 
     According to an embodiment, before operation  603 , the processor  150  of the user terminal  100  may display a second object on the touchscreen display  120  for setting a preset language model as the default speech recognition model. According to an embodiment, when the user selects the second object through the touchscreen display  120 , the processor  150  of the user terminal  100  may set the preset language model as the default speech recognition model. 
     In operation  603 , the user terminal  100  and the intelligence server  200  may determine recognition accuracy for each language model (e.g. an English language model for native Korean speakers, an English language model for native Chinese speakers, etc.). 
     According to an embodiment, operation  603  may include the operation in which the processor  150  of the user terminal  100  receives the user&#39;s speech associated with the displayed plurality of sample texts through the microphone  111 , the operation in which the processor  150  of the user terminal  100  transmits data associated with the received user&#39;s speech to the intelligence server  200  through the communication circuit  160 , the operation in which the processor  220  of the intelligence server  200  performs ASR on data associated with the user&#39;s speech by using a plurality of language models, the operation in which the processor  220  of the intelligence server  200  transmits the results of the ASR to the user terminal  100 , and the operation in which the processor  150  of the user terminal  100  determines the recognition accuracy for each language model based on the results of the ASR. 
     In operation  605 , the processor  150  of the user terminal  100  may select one of language models based on the results from the ASR. 
     In operation  607 , the processor  150  of the user terminal  100  may use the selected language model as the default speech recognition model. 
     According to an embodiment, after operation  607 , the processor  150  of the user terminal  100  may display a message on the touchscreen display  120  indicating that settings of the default speech recognition model is completed. 
     Operation  605  and operation  607  are different from operation  405  and operation  407  in that the processor  220  of the intelligence server  200  transmits the results of the ASR to the user terminal  100  and the user terminal  100  is the system that processes the results of the ASR. Thus, either the processor  220  of the intelligence server  200  or the processor  150  of the user terminal  100  may perform operations  405 / 605  and  407 / 607 . 
       FIG. 7  is a flowchart illustrating operations for determining recognition accuracy for various language models, according to an embodiment. 
     According to an embodiment, operations illustrated in  FIG. 7  may correspond to the described operation  603  of  FIG. 6 . According to an embodiment, embodiments capable of being applied to operation  603  may be also applied to operation  701  to operation  755 . 
     In operation  701 , the processor  150  of the user terminal  100  may receive data of the user&#39;s first speech through the microphone  111 . The first speech may be associated with a first sample text, and the first sample text may be one of a plurality of sample texts. 
     In operation  703 , the processor  150  of the user terminal  100  may transmit data associated with the user&#39;s first speech to the intelligence server  200  through the communication circuit  160 . In other words, in operation  751 , the processor  220  of the intelligence server  200  may receive the data associated with the user&#39;s first speech through the network interface  210 . 
     In operation  753 , the processor  220  of the intelligence server  200  may perform ASR on the data of the user&#39;s first speech by using a plurality of language models (e.g. an English language model for native Korean speakers, an English language model for native Chinese speakers, etc.). 
     According to an embodiment, the processor  220  of the intelligence server  200  may further determine the accuracy of the ASR based on the first sample text and the user&#39;s first speech. According to an embodiment, the accuracy may not be determined by the processor  220  of the intelligence server  200  but may be determined by the processor  150  of the user terminal  100  after operation  705 . 
     In operation  755 , the processor  220  of the intelligence server  200  may transmit the result of the ASR to the user terminal  100 . In other words, in operation  705 , the processor  150  of the user terminal  100  may receive the speech recognition result through the communication circuit  160 . 
     According to an embodiment, the processor  220  of the intelligence server  200  may further transmit the determined accuracy of the ASR to the user terminal  100 . 
     According to an embodiment, the processor  150  of the user terminal  100  may determine the accuracy of the ASR based on the first sample text and the user&#39;s first speech. According to an embodiment, the processor  150  of the user terminal  100  may determine the error rate of the ASR based on the first sample text and the user&#39;s first speech, as described above. According to an embodiment, the processor  150  of the user terminal  100  may determine the confidence level of the ASR based on the result of the ASR performed on the data of the user&#39;s first speech, as described above. According to an embodiment, the processor  150  of the user terminal  100  may determine the accuracy of the ASR based on at least one of the determined error rate and/or confidence level. 
     In the case where the accuracy is less than a critical value in operation  707 , in operation  701 , the processor  150  of the user terminal  100  may re-receive the user&#39;s first speech through the microphone  111 . 
     In the case where the accuracy is not less than the critical value in operation  707 , in operation  709 , the processor  150  of the user terminal  100  may determine the recognition accuracy for each language model. 
     According to an embodiment, in the case where the accuracy is not less than the critical value, the processor  150  of the user terminal  100  may display an indicator (e.g., check mark) at the location of the touchscreen display  120  corresponding to the first sample text. 
     In operation  711 , the processor  150  of the user terminal  100  may receive the user&#39;s second speech associated with the second sample text through the microphone  111 . The second sample text may be different from the first sample text and may be another one of the plurality of sample texts. 
     According to an embodiment, assuming that the user&#39;s second speech associated with the second sample text is received at operation  701 , the above-described operation  701  to operation  711  may be repeatedly performed until the all the plurality of sample texts displayed on the touchscreen display  120  of the user terminal  100  is processed. 
       FIGS. 8A to 8F  are views illustrating screens displayed in a display  120  while the user terminal  100  sets a default speech recognition model, according to an embodiment. 
       FIGS. 8A to 8F  illustrate screens displayed by the user terminal  100  while operation  401  to operation  407  or operation  601  to operation  607  are performed. 
       FIG. 8A  may be a screen corresponding to operation  401  and operation  601 . Referring to  FIG. 8A , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  811  for inducing the user to enter a user input to select a first object, a plurality of sample texts  812 , a first object  813 , and a second object  814 . 
     According to an embodiment, when the user input to select the first object is received, the processor  150  of the user terminal  100  may display a screen as illustrated in  FIG. 8B . 
     According to an embodiment, when the user input to select the second object is received, the processor  150  of the user terminal  100  may allow the intelligence server  200  to use a preset language model as a default speech recognition model. According to an embodiment, when the user terminal  100  selects the default speech recognition model, and when the user input to select the second object is received, the processor  150  of the user terminal  100  may use the preset language model as the default speech recognition model. For example, the preset language model may be an American English speech model. When the second object is selected, operation  403  to operation  407  or operation  603  to operation  607  may not be performed, and the American English speech model may be used as the default speech recognition model. 
     According to an embodiment, the second object may be displayed in  FIGS. 8B to 8E . Thus, when a user input to select the second object is received at any stage, the preset language model may be used as the default speech recognition model. 
       FIG. 8B  illustrates a screen corresponding to operation  501  and operation  701 . Referring to  FIG. 8B , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  821  for guiding the user to speak a first sample text, a first sample text  822  displayed to be visually different from other sample texts, a first object  823  displayed as being activated, and a second object  824 . 
     According to an embodiment, the processor  150  of the user terminal  100  may display the first sample text  822  to be different from other sample texts. For example, the first sample text  822  may be bolded or have a different color and/or size. 
     According to an embodiment, the processor  150  of the user terminal  100  may display the first object  823  as being activated for indicating that the reception of the user&#39;s speech is activated. 
       FIG. 8C  illustrates a screen corresponding to operation  509  and operation  711 . Referring to  FIG. 8C , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  831  for guiding the user to speak a second sample text, an indicator  832  displayed at a location corresponding to the first sample text, a second sample text  833  displayed to be visually different from other sample texts, a first object  834  displayed as being activated, and a second object  835 . 
     According to an embodiment, the processor  150  of the user terminal  100  may display the indicator  832  indicating that the user&#39;s first speech has been recognized. The indicator  832  may be displayed at a location corresponding to the first sample text. 
       FIG. 8D  illustrates a screen for receiving a user&#39;s speech associated with a fifth sample text while the operations shown in  FIGS. 4 and 6  are performed repeatedly. Referring to  FIG. 8D , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  841  for guiding the user to speak a fifth sample text, indicators  842  respectively displayed at locations corresponding to first to fourth sample texts, the fifth sample text  843  displayed to be visually different from other sample texts, a first object  844  displayed as being activated, and a second object  845 . 
       FIG. 8E  illustrates a screen that may be displayed while operation  405  and operation  605  are performed. Referring to  FIG. 8E , the processor  150  of the user terminal  100  may display a message  851  indicating that data associated with the user&#39;s speech are stored, indicators  852  respectively displayed at locations corresponding to first to fifth sample texts, and a second object  853 . 
       FIG. 8F  is a screen indicating that setting of a speech recognition model is completed. Referring to  FIG. 8F , the processor  150  of the user terminal  100  may display a message  861  indicating that settings of a speech recognition model is completed, and a third object  862 . 
     According to an embodiment, when a user input to select the third object  862  is received, the processor  150  of the user terminal  100  may end execution of the operations disclosed herein. 
       FIG. 9A  is a view illustrating a screen displayed while the user terminal  100  waits for reception of the user&#39;s speech. 
       FIG. 9A  is a screen corresponding to operation  501  and operation  701 , and may be the same screen as the one illustrated in  FIG. 8B . Referring to  FIG. 9A , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  911  for guiding the user to speak a first sample text, a first sample text  912  displayed to be visually different from other sample texts, a first object  913  displayed as being activated, and a second object  914 . The second object of  FIGS. 9A to 9C  may be the same as the second object of FIGS.  8 A to  8 E. 
       FIG. 9B  is a view illustrating a screen that the user terminal  100  displays when accuracy of ASR is lower than a critical value, according to an embodiment. 
       FIG. 9B  is a screen that may be displayed when operation  501  is performed after operation  507  (i.e. when the first response is received by the user terminal  100 ). In this case, the processor  150  of the user terminal  100  displays this screen because the accuracy of the ASR corresponding to the previously received user speech is lower than the critical value. 
     Referring to  FIG. 9B , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  921  for guiding the user to enter a user input to select a first object, a first sample text  922  displayed to be visually different from other sample texts, a first object  923 , and a second object  924 . 
     In an embodiment, in the case where the accuracy is lower than the critical value, the processor  150  of the user terminal  100  may display the first object  923  as being deactivated again. The processor  150  of the user terminal  100  may further display the guide message  921  to guide the user to enter a user input to select the first object. Once selected, the user may speak the first sample text again. 
       FIG. 9C  is a view illustrating a screen that the user terminal  100  displays when a standby time for receiving the user&#39;s speech exceeds a specified time period, according to an embodiment. 
       FIG. 9C  is a screen that may be displayed when a specified time period elapses after the processor  150  of the user terminal  100  has displayed a screen as illustrated in  FIG. 9A  on the touchscreen display  120 . 
     Referring to  FIG. 9C , the processor  150  of the user terminal  100  may display, on the touchscreen display  120 , a guide message  931  for guiding the user to enter a user input to select a first object, a first sample text  932  displayed to be visually different from other sample texts, a first object  933 , and a second object  934 . 
     In an embodiment, when, in waiting for the user to enter speech, the processor  150  of the user terminal  100  has stood by for a time period greater than a specified time period, the processor  150  of the user terminal  100  may display the first object  933  as being deactivated again and may display the guide message  931  to guide the user to enter a user input to select the first object  933 . Once selected, the user may speak the first sample text. 
       FIG. 10  is a block diagram of an electronic device  1001  in a network environment  1000  according to an embodiment. Referring to  FIG. 10 , the electronic device  1001  (e.g., the user terminal  100 ) may communicate with an electronic device  1002  through a first network  1098  (e.g., a short-range wireless communication) or may communicate with an electronic device  1004  or a server  1008  (e.g., the intelligence server  200 ) through a second network  1099  (e.g., a long-distance wireless communication) in the network environment  1000 . According to an embodiment, the electronic device  1001  may communicate with the electronic device  1004  through the server  1008 . According to an embodiment, the electronic device  1001  may include a processor  1020 , a memory  1030 , an input device  1050 , a sound output device  1055 , a display device  1060 , an audio module  1070 , a sensor module  1076 , an interface  1077 , a haptic module  1079 , a camera module  1080 , a power management module  1088 , a battery  1089 , a communication module  1090 , a subscriber identification module  1096 , and an antenna module  1097 . According to some embodiments, at least one (e.g., the display device  1060  or the camera module  1080 ) among components of the electronic device  1001  may be omitted or other components may be added to the electronic device  1001 . According to some embodiments, some components may be integrated and implemented as in the case of the sensor module  1076  (e.g., a fingerprint sensor, an iris sensor, or an illuminance sensor) embedded in the display device  1060  (e.g., a display). 
     The processor  1020  may operate, for example, software (e.g., a program  1040 ) to control at least one of other components (e.g., a hardware or software component) of the electronic device  1001  connected to the processor  1020  and may process and compute a variety of data. The processor  1020  may load a command set or data, which is received from other components (e.g., the sensor module  1076  or the communication module  1090 ), into a volatile memory  1032 , may process the loaded command or data, and may store result data into a nonvolatile memory  1034 . According to an embodiment, the processor  1020  may include a main processor  1021  (e.g., a central processing unit or an application processor) and a coprocessor  1023  (e.g., a graphic processing device, an image signal processor, a sensor hub processor, or a communication processor), which operates independently from the main processor  1021 , additionally or alternatively uses less power than the main processor  1021 , or is specified to a designated function. In this case, the coprocessor  1023  may operate separately from the main processor  1021  or embedded. 
     In this case, the coprocessor  1023  may control, for example, at least some of functions or states associated with at least one component (e.g., the display device  1060 , the sensor module  1076 , or the communication module  1090 ) among the components of the electronic device  1001  instead of the main processor  1021  while the main processor  1021  is in an inactive (e.g., sleep) state or together with the main processor  1021  while the main processor  1021  is in an active (e.g., an application execution) state. According to an embodiment, the coprocessor  1023  (e.g., the image signal processor or the communication processor) may be implemented as a part of another component (e.g., the camera module  1080  or the communication module  1090 ) that is functionally related to the coprocessor  1023 . The memory  1030  may store a variety of data used by at least one component (e.g., the processor  1020  or the sensor module  1076 ) of the electronic device  1001 , for example, software (e.g., the program  1040 ) and input data or output data with respect to commands associated with the software. The memory  1030  may include the volatile memory  1032  or the nonvolatile memory  1034 . 
     The program  1040  may be stored in the memory  1030  as software and may include, for example, an operating system  1042 , a middleware  1044 , or an application  1046 . 
     The input device  1050  may be a device for receiving a command or data, which is used for a component (e.g., the processor  1020 ) of the electronic device  1001 , from an outside (e.g., a user) of the electronic device  1001  and may include, for example, a microphone (e.g., the microphone  111 ), a mouse, or a keyboard. 
     The sound output device  1055  may be a device for outputting a sound signal to the outside of the electronic device  1001  and may include, for example, a speaker used for general purposes, such as multimedia play or recordings play, and a receiver used only for receiving calls. According to an embodiment, the receiver and the speaker may be either integrally or separately implemented. 
     The display device  1060  may be a device for visually presenting information to the user and may include, for example, a display, a hologram device, or a projector and a control circuit for controlling a corresponding device. According to an embodiment, the display device  1060  may include a touch circuitry or a pressure sensor for measuring an intensity of pressure on the touch. 
     The audio module  1070  may convert a sound and an electrical signal in dual directions. According to an embodiment, the audio module  1070  may obtain the sound through the input device  1050  or may output the sound through an external electronic device (e.g., the electronic device  1002  (e.g., a speaker or a headphone)) wired or wirelessly connected to the sound output device  1055  or the electronic device  1001 . 
     The sensor module  1076  may generate an electrical signal or a data value corresponding to an operating state (e.g., power or temperature) inside or an environmental state outside the electronic device  1001 . The sensor module  1076  may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor. 
     The interface  1077  may support a designated protocol wired or wirelessly connected to the external electronic device (e.g., the electronic device  1002 ). According to an embodiment, the interface  1077  may include, for example, an HDMI (high-definition multimedia interface), a USB (universal serial bus) interface, an SD card interface, or an audio interface. 
     A connection terminal  1078  may include a connector that physically connects the electronic device  1001  to the external electronic device (e.g., the electronic device  1002 ), for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector). 
     The haptic module  1079  may convert an electrical signal to a mechanical stimulation (e.g., vibration or movement) or an electrical stimulation perceived by the user through tactile or kinesthetic sensations. The haptic module  1079  may include, for example, a motor, a piezoelectric element, or an electric stimulator. 
     The camera module  1080  may shoot a still image or a video image. According to an embodiment, the camera module  1080  may include, for example, at least one lens, an image sensor, an image signal processor, or a flash. 
     The power management module  1088  may be a module for managing power supplied to the electronic device  1001  and may serve as at least a part of a power management integrated circuit (PMIC). 
     The battery  1089  may be a device for supplying power to at least one component of the electronic device  1001  and may include, for example, a non-rechargeable (primary) battery, a rechargeable (secondary) battery, or a fuel cell. 
     The communication module  1090  may establish a wired or wireless communication channel between the electronic device  1001  and the external electronic device (e.g., the electronic device  1002 , the electronic device  1004 , or the server  1008 ) and support communication execution through the established communication channel. The communication module  1090  may include at least one communication processor operating independently from the processor  1020  (e.g., the application processor) and supporting the wired communication or the wireless communication. According to an embodiment, the communication module  1090  may include a wireless communication module  1092  (e.g., a cellular communication module, a short-range wireless communication module, or a GNSS (global navigation satellite system) communication module) (e.g., the communication module  160 ) or a wired communication module  1094  (e.g., an LAN (local area network) communication module or a power line communication module) and may communicate with the external electronic device using a corresponding communication module among them through the first network  1098  (e.g., the short-range communication network such as a Bluetooth, a Wi-Fi direct, or an IrDA (infrared data association)) or the second network  1099  (e.g., the long-distance wireless communication network such as a cellular network, an internet, or a computer network (e.g., LAN or WAN)). The above-mentioned various communication modules  1090  may be implemented into one chip or into separate chips, respectively. 
     According to an embodiment, the wireless communication module  1092  may identify and authenticate the electronic device  1001  using user information stored in the subscriber identification module  1096  in the communication network. 
     The antenna module  1097  may include one or more antennas to transmit or receive the signal or power to or from an external source. According to an embodiment, the communication module  1090  (e.g., the wireless communication module  1092 ) may transmit or receive the signal to or from the external electronic device through the antenna suitable for the communication method. 
     Some components among the components may be connected to each other through a communication method (e.g., a bus, a GPIO (general purpose input/output), an SPI (serial peripheral interface), or an MIPI (mobile industry processor interface)) used between peripheral devices to exchange signals (e.g., a command or data) with each other. 
     According to an embodiment, the command or data may be transmitted or received between the electronic device  1001  and the external electronic device  1004  through the server  1008  connected to the second network  1099 . Each of the electronic devices  1002  and  1004  may be the same or different types as or from the electronic device  1001 . According to an embodiment, all or some of the operations performed by the electronic device  1001  may be performed by another electronic device or a plurality of external electronic devices. When the electronic device  1001  performs some functions or services automatically or by request, the electronic device  1001  may request the external electronic device to perform at least some of the functions related to the functions or services, in addition to or instead of performing the functions or services by itself. The external electronic device receiving the request may carry out the requested function or the additional function and transmit the result to the electronic device  1001 . The electronic device  1001  may provide the requested functions or services based on the received result as is or after additionally processing the received result. To this end, for example, a cloud computing, distributed computing, or client-server computing technology may be used. 
     According to an embodiment of the present disclosure, a system may include at least one network interface, at least one processor operatively connected to the network interface, and at least one memory operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory may further store instructions that, when executed, cause the processor to receive a user&#39;s speech data associated with a plurality of sample texts from an external electronic device displaying a user interface including the plurality of sample texts, where the plurality of sample texts includes words, phrases, and/or sentences, to perform automatic speech recognition (ASR) on the speech data, using the plurality of language models, to select the language model from the plurality of language models based on results from the performed ASR, and to use the selected one language models as a default language model for speech of the user. 
     In an embodiment, the external electronic device may be a mobile device that includes a touchscreen display, and the user interface may be displayed on the touchscreen display. 
     In an embodiment, the instructions may further cause the processor to determine scores for each of the plurality of language models based on the results from the ASR and to use the scores to select the one language model from the plurality of language models. 
     In an embodiment, the instructions may further cause the processor to weigh the scores by applying a weight to each of the scores, wherein the weight is based on at least one of a sample text and a language model corresponding to each of the scores, to determine recognition accuracy for each language model based on the weighted scores; and to use the recognition accuracy to select the one language model from the plurality of language models. 
     In an embodiment, the first language may be English, and the native language may include at least one of Korean, Chinese, Japanese, Hindi, French, British English, German, Spanish, Portuguese, and Russian. 
     In an embodiment, the instructions may further cause the processor to receive, from the external electronic device through the network interface, data of the user&#39;s first speech associated with a first sample text in the plurality of sample texts, to perform the ASR on the data of the user&#39;s first speech using the plurality of language models, to determine accuracy of the ASR based on the first sample text and the user&#39;s first speech, when the accuracy is less than a critical value, to transmit a first response to the external electronic device through the network interface, and to re-receive the data of the user&#39;s first speech from the external electronic device through the network interface, and when the accuracy is equal to or greater than the critical value, to transmit a second response to the external electronic device through the network interface, where the second response is different from the first response, and to receive, from the external electronic device through the network interface, data of the user&#39;s second speech associated with a second sample text in the plurality of sample texts, where the second sample text is different from the first sample text. 
     In an embodiment, the instructions may further cause the processor to receive personalization information of the user from the external electronic device through the network interface and to select the one language model from the plurality of language models based on the received personalization information and the results from the ASR. 
     In an embodiment, the instructions may further cause the processor to select one or more first language models from the plurality of language models based on the personalization information of the user, to select a second language model from the plurality of language models based on the results from the ASR, and when the second language model is included in the one or more first language models, to select the second language model as the default language model. 
     According to an embodiment of the present disclosure, an electronic device may include a housing, a touchscreen display disposed inside the housing and exposed through a first portion of the housing, a microphone disposed inside the housing and exposed through a second portion of the housing, a wireless communication circuit disposed inside the housing, a processor disposed inside the housing and operatively connected to the touchscreen display, the microphone, and the wireless communication circuit, and a memory disposed inside the housing and operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory further stores instructions that, when executed, cause the processor to display a plurality of sample texts on the touchscreen display, to receive, through the microphone, a plurality of a user&#39;s speeches each associated with one of the plurality of sample texts, to transmit data of the plurality of the user&#39;s speeches to an external server through the wireless communication circuit, to receive results from ASR performed on the data of the plurality of the user&#39;s speeches from the external server through the wireless communication circuit, to select one language model from the plurality of language models based on the received results from the ASR, and to use the selected one language model as a default language model for speech of the user. 
     In an embodiment, the instructions may further cause the processor to determine scores for each of the plurality of language models based on the results from the performed ASR and to use the scores to select the one language model from the plurality of language models. 
     In an embodiment, the instructions may further cause the processor to weigh the scores by applying a weight to each of the scores, wherein the weight is based on at least one of a sample text and a language model corresponding to each of the scores, to determine recognition accuracy for each language model based on the weighted scores, and to use the recognition accuracy to select the one language model from the plurality of language models. 
     In an embodiment, the instructions may further cause the processor to receive, through the microphone, data of the user&#39;s first speech associated with a first sample text in the plurality of sample texts, to perform the ASR on the data of the user&#39;s first speech using the plurality of language models, to determine accuracy of the ASR based on the first sample text and the user&#39;s first speech, when the accuracy is less than a critical value, to re-receive the user&#39;s first speech through the microphone, and when the accuracy is equal to or greater than the critical value, to receive, through the microphone, the user&#39;s second speech associated with a second sample text in the plurality of sample texts, where the second sample text is different from the first sample text. 
     In an embodiment, the memory may store personalization information of the user, and the instructions may further cause the processor to select the one language model from the plurality of language models based on the personalization information and the results from the ASR. 
     In an embodiment, the instructions may further cause the processor to select one or more first language models from the plurality of language models based on the personalization information of the user, to select a second language model from the plurality of language models based on the results from the ASR, and when the second language model is included in the one or more first language models, to select the second language model as the default language model. 
     According to an embodiment of the present disclosure, an electronic device may include a housing, a touchscreen display disposed inside the housing and exposed through a first portion of the housing, a microphone disposed inside the housing and exposed through a second portion of the housing, a wireless communication circuit disposed inside the housing, a processor disposed inside the housing and operatively connected to the touchscreen display, the microphone, and the wireless communication circuit, and a memory disposed inside the housing and operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory may further store instructions that, when executed, cause the processor to display a plurality of sample texts on the touchscreen display, to receive, through the microphone, a plurality of a user&#39;s speeches each associated with one of the plurality of sample texts, to transmit data of the plurality of the user&#39;s speeches to an external server through the wireless communication circuit, and to transmit the plurality of language models to the external server. After transmitting the data of the plurality of the user&#39;s speeches and the plurality of language models, the instructions may cause the external server to perform ASR on the data of the plurality of the user&#39;s speeches using the plurality of language models, to select one language model from the plurality of language models based on results from the ASR, and to use the selected one language model as a default language model for speech of the user. 
     In an embodiment, the memory may further store a plurality of text groups respectively corresponding to the plurality of language models, each of the plurality of text groups includes one or more sample texts, and the plurality of sample texts is selected from the plurality of text groups. 
     In an embodiment, the instructions may further cause the processor to display, on the touchscreen display, a first object for activating reception of the plurality of the user&#39;s speeches; and when a user input for selecting the first object is received through the touchscreen display, to receive the plurality of the user&#39;s speeches. 
     In an embodiment, the instructions may further cause the processor to receive, through the microphone. the user&#39;s first speech associated with a first sample text in the plurality of sample texts, to transmit data of the user&#39;s first speech to an external server through the wireless communication circuit, when a first response is received from the external server through the wireless communication circuit, to re-receive the user&#39;s first speech through the microphone, and when a second response different from the first response is received from the external server through the wireless communication circuit, to receive, through the microphone, the user&#39;s second speech associated with a second sample text in the plurality of sample texts, where the second sample text is different from the first sample text. The first response may correspond to accuracy of ASR on the user&#39;s first speech being less than a critical value, and the second response may correspond to the accuracy being equal to or greater than the critical value. 
     In an embodiment, the instructions may further cause the processor, when the second response is received, to display an indicator at a location corresponding to the first sample text. 
     In an embodiment, the instructions may further cause the processor to display, on the touchscreen display, a second object for skipping a setting of the default language model; and when a user input for selecting the second object is received through the touchscreen display, to allow the external server to select a preset language model as the default language model. 
     According to various embodiments of the present disclosure, a system may include at least one network interface, at least one processor operatively connected to the network interface, and at least one memory operatively connected to the processor. The memory may store a plurality of language models of a first language. The plurality of language models may be different from one another. Each of the plurality of language models may be provided for speakers whose native language is different from the first language. The memory may further store instructions that, when executed, cause the processor to provide a user interface including a plurality of samples including words, phrases, and/or sentences, to receive a user&#39;s speech data associated with the plurality of samples, to perform ASR on the speech data by using the plurality of language models, to select one language model from the plurality of language models based on the results from the ASR, and to use the selected language model as a default speech recognition model for speech of the user. 
     The electronic device according to various embodiments disclosed in the present disclosure may be various types of devices. The electronic device may include, for example, at least one of a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a mobile medical appliance, a camera, a wearable device, or a home appliance. The electronic device according to an embodiment of the present disclosure should not be limited to the above-mentioned devices. 
     It should be understood that various embodiments of the present disclosure and terms used in the embodiments do not intend to limit technologies disclosed in the present disclosure to the particular forms disclosed herein; rather, the present disclosure should be construed to cover various modifications, equivalents, and/or alternatives of embodiments of the present disclosure. With regard to description of drawings, similar components may be assigned with similar reference numerals. As used herein, singular forms of words may also include the plural thereof unless the context clearly indicates otherwise. In the present disclosure disclosed herein, the expressions “A or B,” “at least one of A or/and B,” “A, B, or C,” or “one or more of A, B, or/and C,” and the like used herein may include any and all combinations of one or more of the associated listed items. The expressions “a first,” “a second,” “the first,” or “the second” used herein may refer to corresponding components without implying an order of importance, and are used merely to distinguish each component from the others without unduly limiting the components. The above expressions are used merely for the purpose of distinguishing a component from the other components. It should be understood that when a component (e.g., a first component) is referred to as being (operatively or communicatively) “connected,” or “coupled,” to another component (e.g., a second component), it may be directly connected or coupled directly to the other component or any other component (e.g., a third component) may be interposed between them. 
     The term “module” used herein may represent, for example, a unit including one or more combinations of hardware, software and firmware. The term “module” may be interchangeably used with the terms “logic,” “logical block,” “part,” or “circuit.” The “module” may be an integrated part or may be a part thereof for performing one or more functions. For example, the “module” may include an application-specific integrated circuit (ASIC). 
     Various embodiments of the present disclosure may be implemented by software (e.g., the program  1040 ) including instructions stored in a machine-readable storage media (e.g., an internal memory  1036  or an external memory  1038 ) readable by a machine (e.g., a computer). The machine may be a device that calls the instructions from the machine-readable storage media and operates depending on the called instructions and may include the electronic device (e.g., the electronic device  1001 ). When the instructions are executed by the processor (e.g., the processor  1020 ), the processor may perform functions corresponding to the instructions directly or using other components under the control of the processor. The instruction may include code made by a compiler or code executable by an interpreter. The machine-readable storage media may be provided in the form of non-transitory storage media. Here, the term “non-transitory”, as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency. 
     According to an embodiment, the method according to various embodiments disclosed in the present disclosure may be provided as a part of a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or may be distributed only through an application store (e.g., a Play Store™). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or generated in a storage medium such as a memory of a manufacturer&#39;s server, an application store&#39;s server, or a relay server. 
     Each component (e.g., the module or the program) according to various embodiments may include at least one of the above components, and a portion of the above sub-components may be omitted, or additional other sub-components may be further included. Alternatively or additionally, some components (e.g., the module or the program) may be integrated in one component and may perform the same or similar functions performed by each corresponding components prior to the integration. Operations performed by a module, a programming, or other components according to various embodiments of the present disclosure may be executed sequentially, in parallel, repeatedly, or in a heuristic method. Also, at least some operations may be executed in different sequences, omitted, or other operations may be added. 
     Certain aspects of the above-described embodiments of the present disclosure can be implemented in hardware, firmware or via the execution of software or computer code that can be stored in a recording medium such as a CD ROM, a Digital Versatile Disc (DVD), a magnetic tape, a RAM, a floppy disk, a hard disk, or a magneto-optical disk or computer code downloaded over a network originally stored on a remote recording medium or a non-transitory machine readable medium and to be stored on a local recording medium, so that the methods described herein can be rendered via such software that is stored on the recording medium using a general purpose computer, or a special processor or in programmable or dedicated hardware, such as an ASIC or FPGA. As would be understood in the art, the computer, the processor, microprocessor controller or the programmable hardware include memory components, e.g., RAM, ROM, Flash, etc. that may store or receive software or computer code that when accessed and executed by the computer, processor or hardware implement the processing methods described herein. 
     While the present disclosure has been shown and described with reference to various embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents.