Patent Publication Number: US-2010120002-A1

Title: System And Method For Conversation Practice In Simulated Situations

Description:
TECHNICAL FIELD 
     The disclosure generally relates to a system and method for conversation practice in simulated situations. 
     BACKGROUND 
     There exist many varieties of digital systems for conversation practice in simulated situations, such as, script of a conversation, and conversation audio material for learners. A digital teaching material may be usually divided into four types: (1) text and graphic-based, simply displaying text and graphic to the learners, (2) audio-based, by using demonstrated sound of auditory sentence recording able to be played through a player, such as CD/MP3 player, (3) video/audio presentation-based, recording the auditory sentence and the pronunciation image able to be played through a player, such as VCD/DVD player, and (4) computer-based interactive learning software, for the learners to interact with the edited learning material. 
     When using the types (1)-(3) for language learning, the learners usually are shown with the edited course through a playing device, such as the exemplary flow for the conversation material shown in  FIG. 1 . The learner simply repeats the listening and follows the script. In this manner, the learner may only practice the same sentences. Because there are different ways to express the meaning for a same sentence, a learner usually has a hard time to response when the learner is exposed to the actual conversational situation for dialogue and the other use the sentences different from those in the script. Therefore, the language learning using these types of materials usually shows limited results. 
     On the other hand, learning by using computer-based interactive software, the learner and the instructor may have certain extent of interaction. Hence, in a specific environment, the learner may practice repeatedly, and obtain feedback from the interaction through the computer analysis capability. 
     For example, Taiwan Patent No. 468120 disclosed a system and method for learning foreign language verbally. When the system asks a question, the learner may choose an answer from the audio provided by the system. The sentences in the audio are well designed and cover a wide range of different expressions. Taiwan Patent Publication No. 200506764 disclosed an interactive language learning method with language recognition. The user may use audio for response. The response sentence is a fixed sentence set by the system, without description of the conversation flow. Taiwan Patent No. 583609 disclosed a learning system and method with situational character selection and sentence-making, providing the user with selection of character and conversation situation so that the user may learn by following the flow arranged by the system. 
     China Patent Publication No. CN 1720520A disclosed a robotic apparatus with customized conversation system. The system records all the personal information and conversation history of a specific user, and applies all the information to the future conversation of the user. China Patent Publication No. CN 1881206A disclosed a conversation system able to process the user&#39;s re-input appropriately. The system stores the user&#39;s conversation history. When the user is in difference situation and requires the same information, the conversation system may obtain the related information with re-input without inquiring the user again. 
     U.S. Pat. No. 5,810,599 disclosed an interactive audio-video foreign language skills maintenance system. The system may play and pause the pre-prepared multimedia information to achieve the interaction. U.S. Pat. No. 70,522,798 disclosed an automated language acquisition system and method. Based on the hint such as graphic, the user may speak the pre-set words, phrases and sentences in the learning material to perform the evaluation of the user&#39;s pronunciation. However, the interactive conversation between different roles is not described. 
     U.S. Pat. No. 7,149,690 disclosed a method and apparatus for interactive language instruction. According to the sentences inputted by the user, the apparatus may generate conversation images and evaluate the user&#39;s pronunciation. However, the dialogue design of the interactive conversation flow is not disclosed. U.A. Patent Publication No. 20060177802 disclosed an audio conversation device, method, and robotic system to allow the user to converse with the system through audio. The dialogue and the path are pre-defined sentences and flows, and there is no biased error related processing included in the system. 
     SUMMARY 
     The disclosed exemplary embodiments may provide a system and method for conversation practice in simulated situations. 
     In an exemplary embodiment, the disclosed is directed to a system for conversation practice in simulated situations. The system may comprise a situational conversation teaching material, an audio processing module and a conversation processing module. The teaching material consists of multi-flow dialogue paths and dialogue sentences with a plurality of replaceable vocabulary items. The audio processing module dynamically adjusts a speech recognition model according to different contents of the situational conversation teaching material, and recognizes the inputted audio signal of the learners to determine the information on the recognition results. The audio processing module may further refer to biased error information of the teaching material, or synonymy information of the teaching material or any combination of the two of the above, to dynamically adjust the speech recognition mode. The conversation processing module determines the information in response to the learners, based on the information on the recognition results and the situational conversation teaching material. 
     In another exemplary embodiment, the disclosed is directed to a method for conversation practice in simulated situations, comprising: preparing a situational teaching material with multi-flow dialogue paths and dialogue sentences having a plurality of replaceable vocabulary items; dynamically adjusting a speech recognition model according to different contents of the situational conversation teaching material, and recognizes the inputted audio signal of the learners to determine the information on the recognition results; the adjustment of speech recognition model may further referring to biased error information of the teaching material, or synonymy information of the teaching material or any combination of the two of the above; and determining the information in response to the learners, based on the information on the recognition results and the situational conversation teaching material. 
     The foregoing and other features, aspects and advantages of the present invention will become better understood from a careful reading of a detailed description provided herein below with appropriate reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a schematic view of an exemplary conversation flow of a conventional conversation teaching material. 
         FIG. 2  shows an exemplary schematic view of a system for conversation practice in simulated situations, consistent with certain disclosed embodiments. 
         FIG. 3  shows an exemplary flowchart of a method for conversation practice in simulated situations, consistent with certain disclosed embodiments. 
         FIG. 4  shows an exemplar for explaining the conversation nodes and connection lines, consistent with certain disclosed embodiments. 
         FIG. 5  shows an exemplar for explaining the course objective rule and course objective tasks, consistent with certain disclosed embodiments. 
         FIG. 6  shows the features of multi-variation conversation sentences and multi-variation conversation flow for the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 7  shows the feature of multi-variation conversation sentences for the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIGS. 8   a - 8   g  show the definition of the 8 types of connection lines in the multi-path connection rule, consistent with certain disclosed embodiments. 
         FIG. 9  shows an exemplary mapping table of biased sentences, consistent with certain disclosed embodiments. 
         FIG. 10  shows an exemplary schematic view of the structure and detailed operation of the audio processing module, consistent with certain disclosed embodiments. 
         FIG. 11  shows an exemplary schematic view of the structure and detailed operation of the conversation processing module, consistent with certain disclosed embodiments. 
         FIG. 12  shows an exemplar illustrating response pattern sentences for a sentence which is a learner&#39;s pronunciation error, consistent with certain disclosed embodiments. 
         FIG. 13  shows an exemplary schematic view of the structure of the output device, consistent with certain disclosed embodiments. 
         FIG. 14  shows a first working example of the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 15  shows exemplary field tables for the variables based on  FIG. 14  of the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 16  shows a second working example of the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 17  shows exemplary field tables for the variables based on  FIG. 16  of the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 18  shows a third working example of the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 19  shows exemplary field tables for the variables based on  FIG. 18  of the situational conversation teaching material, consistent with certain disclosed embodiments. 
         FIG. 20A  shows an exemplar of adding biased error information to the situational conversation teaching material, where the result of the objective sentence is the sentence after variable processing, consistent with certain disclosed embodiments. 
         FIG. 20B  shows an exemplary mapping table between the sentence after adding biased error information and the serial number of error type, consistent with certain disclosed embodiments. 
         FIG. 21  shows an exemplary mapping table of the synonymous sentence, consistent with certain disclosed embodiments. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     In actual world, the conversation between people is often face-to-face, and so is the language learning. When learning a kind of language, the learner usually hopes that the practice is with an actual person or an image of a person so that the contents of the conversation may have different responses based on the learner&#39;s different answers. In the disclosed exemplary embodiments, the system for conversation practice in simulated situations is based on the teaching material with multi-flow dialogue paths and dialogue sentences having a plurality of replaceable vocabulary items to provide the learner with interactive conversation practice. The system provides the learners with nature-like conversation environment to simulate the actual conversation situations. For example, the system provides a synthesized image of human face to simulate the face-to-face conversation, provides a plurality of sentence expressions of the same meaning to avoid the unalterable dialogue response, and provides multi-flow dialogue paths to avoid the unalterable dialogue flow. 
       FIG. 2  shows an exemplary schematic view of a system for conversation practice in simulated situations, consistent with certain disclosed embodiments. Referring to  FIG. 2 , system  200  for conversation in simulated situations comprises a situational teaching material  210 , consisting of multi-flow dialogue paths  210   a  and dialogue sentences  210   b  with a plurality of replaceable vocabulary items, an audio processing module  220  and a conversation processing module  230 . Audio processing module  220  dynamically adjusts a speech recognition model according to different contents of the situational conversation teaching material  210 , and recognizes an inputted audio signal  220   a  of the learners to determine and output information  220   b  on the recognition results to conversation processing module  230 . Conversation processing module  230  determines information  230   b  in response to the learners, based on information  220   b  on the recognition results, situational conversation teaching material  210 , wherein audio processing module  220  may dynamically adjust a speech recognition model based on different contents of situational conversation teaching material  210 , and may further refers to biased error information  230   a  of the teaching material, synonymy information  230   c  of the teaching material, or any combination of the one or two of the above. Conversation processing module  230  may determine information  230   b  in response to the learner based on information  220   b  of the recognition result and situational conversation teaching material  210 , and may further refer to biased error information  230   a  and synonymy information  230   c  of teaching material, or any combination of the one or two of the above, to determine information  230   b  in response to the learner. 
     The speech recognition model is selected from acoustic model, language model, grammar and dictionary. Audio processing module  220  may obtain audio signal  220   a  from the learner through an input module. Conversation processing module  230  may use an output module to output response information, such as text, graphics, audio or images. When the entire conversation practice is over, the output device may be used to output the conversation information of the learner so that the learner may understand the learner&#39;s own practice condition. 
       FIG. 3  shows an exemplary flowchart of a method for conversation practice in simulated situations, consistent with certain disclosed embodiments. Referring to  FIG. 3 , first, it is to prepare situational teaching material  210 , consisting of multi-flow dialogue paths  210   a  and conversation sentences  210   b  with replaceable vocabulary items, as shown in step  302 . The next step  304  is to adjust a speech recognition model according to different contents of situational conversation teaching material  210 , biased error information  230   a  of the teaching material, and synonymy information  230   c  of the teaching material, and recognize inputted audio signal  220   a  of the learners to determine information  220   b  on the recognition results. In step  306 , it is to determine information  230   b  in response to the learners, based on information  220   b  on the recognition results, situational conversation teaching material  210 , biased error information  230   a  of the teaching material, and synonymy information  230   c  of the teaching material. 
     The situational conversation teaching material is generated according to the edition rules of teaching material. The situational conversation teaching material may also includes a database of biased errors. The data in the bias database, called biased error information  230   a , is generated according to the common errors and the teaching material, and may be used by conversation processing module  230  as reference information in determining the correctness of the conversation dialogues used by the learner. The situational conversation teaching material may also include a synonymy database. The data in synonymy database, called synonymy information  230   c , is generated according to the common expressions and vocabulary analysis, and may be used by conversation processing module  230  as a reference in analyzing the synonymy used by the learner so as to dynamically adjust the speech recognition model and recognize the inputted audio signals of the learner. 
     Another exemplary flowchart of the method for conversation practice in simulated situations is to simplify the flow of  FIG. 3 . The first step is to prepare situational teaching material  210 , consisting of multi-flow dialogue paths  210   a  and conversation sentences  210   b  with replaceable vocabulary items, as shown in step  302 . The simplified step  304  is to adjust a speech recognition model according to situational conversation teaching material  210 , and recognize inputted audio signal  220   a  of the learners to determine information  220   b  on the recognition results. Based on information  220   b  on the recognition results and situational conversation teaching material  210 , the response information  230   b  to the learner may be determined. 
     In the disclosed embodiments, each conversation node for the situational conversation teaching material is directionally connected by the node connection lines.  FIG. 4  shows an exemplar for illustrating the conversation node and connection lines, consistent with certain disclosed embodiments. As shown in  FIG. 4 , mark  410  is a conversation node of a general teaching material  400 , and the conversation nodes are directionally connected by node connection line  420 . 
     The edition rules of teaching material may include the course objective rules, multi-path connection rules, multi-variation conversation sentence rules, and so on. The course objective rules, as shown in  FIG. 5 , shows that each course may have a plurality of course objectives, and each course objective may include one or more variables. However, the course objectives describes the required tasks that must be accomplished for each course and the tasks of the course will be a course objective, marked as  501 , randomly selected from all of the objectives of the course. Then, the variable in the course objective is replaced to become the task of this learning, marked as  502 . The designed conversation sentences and the conversation flows will be appropriately arranged targeting the current course objective. When the learner finishes the current course, the accomplishment extent of the course objective after this practice will be shown to understand whether or not the course objective is accomplished in this learning session. 
     With the example of  FIG. 4 ,  FIG. 6  further shows the features of multi-variation conversation sentences and multi-variation conversation flow for the situational conversation teaching material, consistent with certain disclosed embodiments. As shown in  FIG. 6 , the sentences of the course objective may include plural variations, such as, multi-variation sentence  610 , and many variables. Take the sentence “I would like to buy $Var 1  (two pieces of) erasers” as an example. Assume that $Var 1  randomly selects “two pieces of”. So the complete sentence will be “I would like to buy two pieces of erasers.” According to the primitive patterns and revised patterns in  FIG. 21 , “would like to” may be replaced by “want to buy” or “buy”. The synonymous sentence “I would like to buy two pieces of erasers.” may be replaced by “I want to buy two pieces of erasers.” or “Buy two pieces of erasers.”; moreover, “eraser” and “rubber eraser” are synonyms too. Hence, “I want to buy two pieces of rubber erasers”, “I would like to buy two pieces of rubber erasers” and “buy two pieces of rubber erasers.” may all be shown in synonymous sentence  615  of multi-variation sentence  610 . If variable $Var 1  randomly selects “one piece of”, the synonymous sentences are changed to “I want to buy one piece of eraser”, “I want to buy one piece of rubber eraser”, “I would like to buy one piece of eraser”, “I would like to buy one piece of rubber eraser”, “buy one piece of eraser”, and “buy one piece of rubber eraser.” 
     This example also includes biased error information. According to the primitive pattern and revised pattern which are made by teachers shown in  FIG. 9 , for example, it&#39;s a biased error information for foreigners learning mandarin, “two” (liang 3 ) can be mistaken for “double” (er 4 ). The sentence “I would like to buy two pieces of erasers.” (wo 3  xiang 3  yao 4  mai 3  liang 3  kuai 4  xiang 4  pi 2  ca 1 ) will be changed to “I would like to buy double pieces of erasers.” (wo 3  xiang 3  yao 4  mai 3  er 4  kuai 4  xiang 4  pi 2  ca 1 ). “piece of eraser” (kuai 4  xiang 4  pi 2  ca 1 ) can be mistaken for “drop of eraser” (li 4  xiang 4  pi 2  ca 1 ) or “ball of eraser” (ke 1  xiang 4  pi 2  ca 1 ). The sentence also can be changed to “I would like to buy two drops of erasers” (wo 3  xiang 3  yao 4  mai 3  liang 3  li 4  xiang 4  pi 2  ca 1 ), I would like to buy two balls of erasers” (wo 3  xiang 3  yao 4  mai 3  liang 3  ke 1  xiang 4  pi 2  ca 1 ), “I would like to buy double drops of erasers” (wo 3  xiang 3  yao 4  mai 3  er 4  li 4  xiang 4  pi 2  ca 1 ), I would like to buy double balls of erasers.” (wo 3  xiang 3  yao 4  mai 3  er 4  ke 1  xiang 4  pi 2  ca 1 ). All of above biased sentences may be shown in  610   a  of multi-variation sentence  610 . 
     In learning different language, the disclosure provides rules to handle “biased sentences”. The following is another disclosed English embodiment of the disclosure. In the instance of “He is girl”, it includes two biased error information, lack of “a” and “girl” with corresponding “She”. Therefore it would be handled as “She is a girl”. 
     The disclosure is also designed for supporting “mistake sentences”, defined as the mistakes that the learners should have noticed, but not. For an example according “I would like to buy $Var 1  erasers.”, $Var 1  is selected as “two pieces of” randomly. The mistake sentences of the above example will be generated by using the other names in the $Var 1  tables in  FIG. 7 . Three mistake sentences generated are “I would like to buy one piece of erase”, “I would like to buy three pieces of erasers” and “I would like to buy five pieces of erasers”, shown as mark  625  in  FIG. 6 . 
     In this exemplar of situation conversation teaching material, the total number of variables of the course objective sentences is nine, i.e., $Var 1 -$Var 9 , as shown in  FIG. 7 .  FIG. 7  shows the feature of multi-variation conversation sentences for the situational conversation teaching material, consistent with certain disclosed embodiments. Variables $Var 1 -Var 9  have three attributes and two data fields. The three attributes are Random, Get and Total, and the two data fields are Name and Value. The following describe the features of the three attributes. 
     Attribute Random is the most common attribute used in the course production. The function of Random is to perform random selection for variable with attribute Random after the leaner enters the course. Attribute Get is for the highly related variables. The function is to let the variable with attribute Get has the same random reference value as the variable to be got. For example, variable $Var 7  is for total amount and variable $Var 8  is for the pay amount. The pay amount must be always greater than or equal to the total amount; otherwise, the conversation flow will confuse the learner. The use of attribute Get follows the logic of the teaching material editor, and may be used for different purposes, such as the mapping between unit (stick, yard, copy) and noun (pencil, ruler, book). The mapping may also be solved by the use of this attribute. Attribute Total is for simple calculation, such as change, sum, to provide the basic arithmetic calculation. 
     The multi-variation conversation flow, shown as marks  620   a - 620   c  of  FIG. 6 , has three different conversation flows. The conversation sentences and conversation flows are also arranged appropriately according to the course objective. For example, when the course objective selects the sentence “Today, Mr. Tang wants to buy his son $Var 2  (two yards of) rulers and wishes to deliver by $Var 5  (three o&#39;clock) PM. So Mr. Tang visits shopping website . . . ”, the system will remind the learner of the course objective in the practice so that the learner will not get lost during the conversation. Also, after each dialogue, the accomplishment extent of the current course objective is updated in real time. After the conversation, the leaner will be informed whether the course objective is accomplished in this practice. 
     The multi-path connection rule defines 8 types of connection lines for conversation nodes, called Type 1-8 connection lines.  FIGS. 8   a - 8   g  show the definition of Type 1-8 connection lines of the multi-path connection rule. 
     Refer to  FIG. 8   a.  Type 1 connection line represents the flow transferring from conversation node A to conversation node B, shown as marked  80   a . The connection line is a basic connection line and conversation node B connected by the connection line can be transferred to repeatedly. 
     Refer to  FIG. 8   b.  Type 2 connection line represents the flow transferring from conversation node A to conversation node B, and if conversation node B has been transferred to before, then transferring to conversation node B is prohibited, shown as marked  80   b . In other words, the connection line is a basic connection line and conversation node B connected by the connection line cannot be transferred to repeatedly. 
     Refer to  FIG. 8   c.  Type 3 connection line represents that if a conversation node connected to conversation node A, such as conversation node B, of Type 1 and Type 2 connection lines have been transferred to before, the flow will be transferring from conversation node A to conversation node C, shown as marked  80   c . In other words, after either Type 1 or Type 2 connection line which connected on the node has been transferred once, the node of the Type 3 connection line can be taken. 
     Refer to  FIG. 8   d.  Type 4 connection line represents that if all the conversation nodes connected to conversation node A by Type 1 and Type 2 connection lines have been transferred to before, the flow will be transferring from conversation node A to conversation node C, shown as marked  80   d . In other words, after all the Type 1 or Type 2 connection lines which connected on the node have been transferred once, the node of the Type 4 connection line can be taken. 
     Refer to  FIG. 8   e.  Type 5 connection line represents the flow transferring from conversation node A to conversation node B, and the transferring will not affect other types, shown as marked  80   e . In other words, the connection line is not a basic connection line and conversation node B connected by the connection line can be transferred to repeatedly. 
     Refer to  FIG. 8   f.  Type 6 connection line represents the flow transferring from conversation node A to conversation node B, and the transferring will not affect other types. If conversation node B has been transferred to before, transferring to conversation node B is prohibited, shown as marked  80   f . In other words, the connection line is not a basic connection line and conversation node B connected by the connection line cannot be transferred to repeatedly. 
     Refer to  FIG. 8   g.  Type 7 connection line, i.e., the Call connection line, represents the flow connecting from conversation node A (calling node) to conversation node B (starting node). When the flow encounters Type 8 connection line, i.e., the Return connection line, the flow will transfer from conversation node B, passing node A and to node C, shown as marked  80   g . In other words, Type 8 connection line is a connection line returning from starting node to calling node. 
     Multi-variation conversation sentence rules allow a plurality of sentences with the same meaning and yet different expressions to exist in a conversation node so as to increase the situational effect matching the actuality. In addition, variable assignment function can be assigned to different conversation sentences, such as numeric variable, string variable, and providing calculation equations between variables. The course may be more vivid and lively according to the assignment by the teaching material editor. Hence, system  200  for conversation practice in simulated situations has the function of allowing situational conversation teaching material  210  to set course objectives. 
     Bias database of teaching material is obtained by collecting the common biased errors made by the learners and analyzed by professional scholars. The bias database points out the correspondence between the correct sentences and vocabulary versus the erroneous sentences and vocabulary, which may be shown as a mapping table in  FIG. 9 . The biased serial number field records the type and the serial number of the bias, such as ER_Num_ 11 . The primitive pattern field records the pattern edited by the course editor, usually about the correct usage of the language. For example, ER_Num_ 11  corresponds to primitive pattern “copy of notebook”. The revised pattern field records the possible error or other usages of the primitive pattern. For example, ER_Num_ 11  correspond to revised pattern “piece of notebook”. The memo field records the explanation of the rules for reference by the editor. For example, ER_Num_ 11  corresponds to memo: unit “piece” an error, unit for notebook being “copy”. The English memo field records the English description of the rules, which may be changed to the native language of the learner to help the learner to understand the correct use for the erroneous sentence and future reference. Error type field records the type of the bias to which the rule belongs to. 
     Audio processing module  220  may obtain the leaner&#39;s audio signal through an input module, and perform the speech recognition and voice adaption to determine the information in response to the conversation processing module.  FIG. 10  shows an exemplary schematic view of the structure and detailed operation of the audio processing module  220 , consistent with certain disclosed embodiments. Referring to  FIG. 10 , audio processing module  220  comprises a speech recognition module  1021  and an adaption module  1022 . According to the situational conversation teaching material, biased error information of teaching material and synonymy information of teaching material, adaption module  1022  dynamically adjusts the speech recognition model in provision to speech recognition module  1021  for recognizing the learner&#39;s audio signals. Adaption module  1022  also adjusts the acoustic model of the speech recognition model according to the learner&#39;s audio signal for voice adaption, and adjusts the language model, grammar and dictionary to improve the speech recognition according to the situational conversation teaching material, biased error information of teaching material and synonymy information of teaching material. Speech recognition module  1021  transmits information  220   b  of the recognition results to conversation processing module  230  in order to perform corresponding response. The information of the recognition results may be, for example, sentence grade or sentence text. 
     According to the above, step  304  of  FIG. 3  may include the following sub-steps: according to the situational conversation teaching material, biased error information of teaching material and synonymy information of teaching material, dynamically adjusting the speech recognition model for performing speech recognition on the learner&#39;s audio signals; according to the learner&#39;s audio signals, performing voice adaption, and according to the situational conversation teaching material and biased error information of teaching material, adjusting acoustic model of the speech recognition model, and adding synonymy information of teaching material to adjust the language model, grammar and dictionary in the speech recognition model for improving the speech recognition; and then determining and outputting the recognition results of the learner&#39;s audio signal. In another exemplar, the speech recognition model may be directly dynamically adjusted according to the situational conversation teaching material. Alternatively, the speech recognition model may be dynamically adjusted according to the situational conversation teaching material and the biased error information of teaching material. Or, the speech recognition model may be dynamically adjusted according to the situational conversation teaching material and synonymy information of teaching material. 
     Conversation processing module  230  may detect the possible errors of the grammar and pronunciation according to the recognition results and biased error information, and determine the information in response to the learner according to the situational conversation teaching material. The learner&#39;s conversation practice record may also be stored. The conversation practice record may include the conversation sentences, intonation, pronunciation and bias.  FIG. 11  shows an exemplary schematic view of the structure and detailed operation of the conversation processing module, consistent with certain disclosed embodiments. The detailed operation is also the sub-steps of step  306  of  FIG. 3 . 
     As shown in  FIG. 11 , conversation processing module  230  comprises a sentence processing module  1121  and a flow processing module  1122 . According to the information of audio processing module  220  (including situational conversation teaching material  210 , or situational conversation teaching material  210  plus biased error information  230   a  and synonymy information  230   c  of teaching material) and the information of the speech recognition results, sentence processing module  1121  determines whether the conversation sentence of the learner is correct, synonymous, biased or mispronouncing, and records the related information of the conversation sentence to conversation data  1130 . Biased error information of teaching material  230   a  may be information in a database of biased errors, and synonymy information of teaching material  230   c  may be information in a database of synonyms. When the information of audio processing module  220  includes situational conversation teaching material  210  plus biased error information  230   a  or synonymy information  230   c  of teaching material, sentence processing module  1121  may provide the information on whether the learner&#39;s sentence is synonymous, biased or mispronouncing according to the information of audio processing module  220  and the information of recognition results. 
     According to the determination of sentence processing module  1121 , flow processing module  1122  determines the response sentence. If the determination is the conversation sentence of the learner is correct, synonymous or biased, flow processing module  1122  will continue the conversation in the next conversation node of the teaching material. If the determination is the conversation sentence of the learner is mispronouncing, flow processing module  1122  will continue the conversation in pattern sentence  1140  as response. Pattern sentence  1140  is the conversation sentence in response to the learner when the leaner&#39;s sentence is determined to be mispronouncing, as shown in  FIG. 12 . 
     If the learner&#39;s sentence in that conversation node is determined to be mispronouncing again, the flow may be set to continue the conversation sentence in the next conversation node. When executing the next conversation sentence, an output device may be used to output the information of the required response, such as text, graphics, audio, image, and so on.  FIG. 13  shows an exemplary schematic view of the structure of the output device, consistent with certain disclosed embodiments. When the entire conversation practice is over, an output device may be used to show the conversation data so that the learner may understand his/her own practice situation. 
     As shown in  FIG. 13 , output device  1300  may include a data reading module  1310 , or an image-based human face synthesis module  1320 , or an audio synthesis module  1330 . Data reading module  1310  may receive and display the output data from conversation processing module  230 . If the received data is text or audio, image-based human face synthesis module  1320  may be used to generate a human face and an integrated audiovisual may be outputted, or audio synthesis module  1330  may be used to synthesize the text data into audio for output. 
     The following shows three working examples to describe the flow of the situational conversation teaching material, Type 1-8 connection lines of multi-path connection rules and the design of situational conversation teaching material. 
     In the first working example, the course objective is that Mr. Tang would like to buy three red pens, three bottles of ink, and notebook at a shopping website. Through situational conversation teaching material  210 , the course objective may be set as “Mr. Tang would like to buy $Var 1  (3) $Var 2  (red) pens, $Var 7  (3) $Var 4  (bottles of ink) and $Var 3  (notebook).” In other words, system  200  for conversation practice in simulated situations allows the function of setting course objective. 
     According to the aforementioned defined 8 types of connection lines, an exemplary flow of the situational conversation teaching material may be generated in  FIG. 14 . In the exemplary flow, according to the multi-path connection rules, the flow passes may include Type 1 connection line, Type 2 connection line, Type 3 connection line, Type 4 connection line, Type 7 connection line, and Type 8 connection line. 
     As seen in the exemplary flow of  FIG. 14 , the conversation script of the situational conversation teaching material at least includes the design of conversation node, conversation flow, conversation sentence, synonymous sentence, biased sentence, and mistake sentence. The conversation data also requires, for example, variable table, for recording.  FIG. 15  shows an exemplary field tables for the variables based on  FIG. 14  of the situational conversation teaching material, consistent with certain disclosed embodiments. 
     There are seven variable field tables in the exemplar of  FIG. 15 , $Var 1 -$Var 7 . Variable field $Var 1  is a string variable for recording the possible number of pens, such as 2, 3, 4, 5, 6. Variable field $Var 2  is a string variable for recording the possible color of pens, such as, blue, black, red. Variable field $Var 7  is a string variable for recording the possible number of bottles of ink, such as, 1, 2, 3. Variable field $Var 6  uses attribute Get to obtain the random reference value the same as variable field $Var 3 . In this exemplar, the randomly selected values for each variable field are: $Var 1 =“3”, $Var 2 =“red”, $Var 3 =“notebook”, $Var 4 =“bottles of ink”, $Var 5 =“4”, $Var 6 =“copies of notebooks”, and $Var 7 =“3”. 
     As seen in  FIGS. 14-15 , a conversation node may include sentences with same meaning but of different expressions. Also, different conversation sentences may have different variable setting functions, such as numeric variables, string variables, and collaborative use of variables. Situational conversation teaching material  210  also allows the functions of sharing the same variable name between course objective and the sentence in conversation node. Hence, the course may be more vivid and lively and create situations effect matching actuality. 
     In the second working example, the course objective is that Mr. Tang would like to buy two red pens, and one pack of brown paper at a shopping website. If the red pens are sold out, two blue pens will do. Through situational conversation teaching material  210 , the course objective may be set as “Mr. Tang would like to buy $Var 1  (two sticks of) $Var 2  (red pens), if $Var 2  (red pens) are sold out, $Var 1  (two sticks of) !$Var 2  (blue pens) will do, and $Var 3  (one pack of) $Var 4  (brown paper). It is worth noting another feature of the multi-variation conversation sentence. The difference between $Var 2  (red pens) and !$var 2  (blue pens) is the “!” symbol indicating the variable with attribute Random to deduct the current selection when randomly selecting a reference value again. For example, $Var 2  randomly selects “red pens”, and then !$Var 2  will randomly select from “blue pens” and “black pens” but “red pens”. 
     According to the aforementioned defined 8 types of connection lines, an exemplary flow of the situational conversation teaching material may be generated in  FIG. 16 . In the exemplary flow, according to the multi-path connection rules, the flow passes may include Type 1 connection line, Type 2 connection line, and Type 4 connection line.  FIG. 17  shows exemplary field tables for the variables based on  FIG. 16  of the situational conversation teaching material, consistent with certain disclosed embodiments. 
     As seen in  FIG. 17 , the calculation equation between variables, such as $Var 5  having an attribute Total, and the numeric variable is equal to $Var 1 .Value*$Var 2 .Value+$Var 3 .Value*$Var 4 .Value. When the randomly selected fields are “two red pens+a pack of brown paper”, the result of the calculation is 2×10+1×90=110. Therefore, system  200  for conversation practice in simulated situations provides the calculation equations between variables. In addition, $!Var 2  may select “blue pens” after deducting the last random selection, and $!var 4  may selects “drawing paper”. In this manner, the conversation sentences may vary because vocabulary may be replaced. Therefore, system  200  may raise the learner&#39;s interest through various simulated situations. 
     In the third working example, the course objective is that Mr. Tang would like to buy four yards of rulers, five sticks of pens, seven copies notebooks and three pieces of erasers at a shopping website, and wishes the items may be delivered to his house by two o&#39;clock. In addition, Mr. Tang&#39;s son would like to buy a sport item if Mr. Tang sees any. Through situational conversation teaching material  210 , the course objective may be set as “Mr. Tang would like to buy $T 1  (four yards of) rulers, $T 2  (five sticks of) pens, $T 3  (seven copies of) notebooks and $T 4  (three pieces of) erasers, and wishes the items to be delivered by $T 5  (two) o&#39;clock. In addition, Mr. Tang&#39;s son would like to buy a sport item if Mr. Tang sees any.” 
     According to the aforementioned defined 8 types of connection lines, an exemplary flow of the situational conversation teaching material may be generated in  FIG. 18 . In the exemplary flow, according to the multi-path connection rules, the flow passes may include Type 1 connection line, Type 2 connection line, Type 4 connection line, and Type 6 connection line.  FIG. 19  shows exemplary field tables for the variables based on  FIG. 18  of the situational conversation teaching material, consistent with certain disclosed embodiments. 
     There are five variable field tables in the exemplar of  FIG. 19 , $T 1 -$T 5 . Variable fields $T 1 -$T 5  are all string variables, for example, $T 1  records the number and the unit of the ruler, such as, one yard of, three yards of, four yards of, seven yards of, nine yards of. $T 2  records the number and the unit of the pen, such as, three sticks of, five sticks of, seven sticks of. $T 5  records the time for item delivery, using hours as the unit. 
       FIGS. 20A-B  show an exemplar of adding biased error information to the situational conversation teaching material, biased error information, and the result of the exemplar is the objective sentence plus biased sentence, consistent with certain disclosed embodiments. According to the teaching material editor input sentence in the tables in  FIG. 20A , the system performs the replacement and refers to  FIG. 9  to replace the replaced sentence with the primitive pattern and revised pattern to find the corresponding error type serial number to generate  FIG. 20B . The biased serial number in  FIG. 20B  may refer to note field in  FIG. 9  to provide the explanation and correct usage for the biased error sentence to the learner. 
     In summary, the disclosed embodiments may provide a system and method for conversation practice in simulated situations. The teaching material editor may use the disclosure to design the situational conversation teaching material with multi-variation conversation sentences and multi-variation conversations flows. Each conversation teaching material may be set with conversation objectives and replaceable vocabulary of the conversation sentences so that the conversation sentence may include different variations, while the conversation flow also depends on the learner&#39;s response. With the system of conversation practice in simulated situations, the learner may interact with the synthesized human face in a simulated situation. When the learner makes a biased error, the information may be recorded and displayed after finishing the conversation practice. The errors made by the learner may be pointed out. 
     Although the present invention has been described with reference to the exemplary embodiments, it will be understood that the invention is not limited to the details described thereof. Various substitutions and modifications have been suggested in the foregoing description, and others will occur to those of ordinary skill in the art. Therefore, all such substitutions and modifications are intended to be embraced within the scope of the invention as defined in the appended claims.