CN114841841A - Intelligent education platform interaction system and interaction method for teaching interaction - Google Patents
Intelligent education platform interaction system and interaction method for teaching interaction Download PDFInfo
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Abstract
The invention discloses an intelligent education platform interaction system and an interaction method for teaching interaction. The intelligent education platform interactive system comprises an intelligent education platform, a teacher end and a plurality of student ends, wherein the intelligent education platform is connected with the teacher end and the student ends through network communication. The voice interactive teaching system further comprises a voice recognition module, voice recognition is carried out through voice interactive information in the teaching process, text information is formed and fed back to teachers and students in real time, transmission of interactive information is assisted, interaction of texts is achieved while voice interaction is carried out, the problem of unsmooth interaction caused by network fluctuation is avoided, and teaching experience is improved.
Description
Technical Field
The invention relates to the technical field of intelligent education platforms, in particular to an intelligent education platform interaction system and an interaction method for teaching interaction.
Background
Along with the development of school education informatization and mobile internet, online teaching platforms are more and more applied to the teaching process. At present, network delay often exists in the process of network teaching, and the problem of confusion of the interaction process caused by simultaneous speaking of a plurality of persons exists. For example, in the process of answering the teacher's question, the voices of some students cannot be completely and clearly obtained by the teacher due to the network, and the teaching voices of the teacher are missed by the students due to the same reason. Sometimes, the teacher receives many classmates of voice information, so that the voice content of each person is not heard clearly.
In addition, the existing voice recognition technology is not specially used in the field of education and teaching, and based on the existing voice recognition technology, the requirements for instant display and recording of voice interaction contents in the online teaching process cannot be met.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent education platform interaction system and an interaction method for teaching interaction, which can quickly convert interactive voice into text by improving a voice recognition algorithm, improve the effectiveness and accuracy of an online teaching interaction process, correct the converted voice text, record the teaching process while improving the interaction fluency, facilitate a teacher to analyze the classroom situation and facilitate students to review the classroom learning content.
In order to achieve the purpose, the invention adopts the following technical scheme:
the interactive system of the intelligent education platform for teaching interaction is characterized by comprising the intelligent education platform, a teacher end and a plurality of student ends, wherein the intelligent education platform is in communication connection with the teacher end and the student ends through a network;
the intelligent education platform comprises a login module, a language model database and a data processing module;
the teacher side includes: the display module comprises a teaching process recording area for displaying teaching records; the audio output module is used for outputting the received voice information; the voice input module is used for receiving voice information of a teacher end; the video input module is used for shooting the teaching process of a teacher; the first voice recognition module is used for quickly recognizing the voice information of the user and converting the voice information into text information; the second voice recognition module is used for accurately recognizing the voice information of the user and correcting the text information obtained by quick recognition;
the student terminal includes: the display module comprises a teaching process recording area for displaying teaching records; the audio output module is used for outputting the received voice information; the voice input module is used for receiving the voice information of the student end; the video input module is used for shooting the learning process of students; the first voice recognition module is used for quickly recognizing the voice information of the user and converting the voice information into text information; and the second voice recognition module is used for accurately recognizing the voice information of the user and correcting the text information obtained by quick recognition.
Preferably, the intelligent education platform further comprises a storage module for storing the language model database, the teaching courseware, the teaching process video and the data.
Preferably, the first speech recognition module adopts speech recognition based on a unidirectional recurrent neural network to obtain a real-time recognition result.
Preferably, the second speech recognition module adopts speech recognition based on a bidirectional recurrent neural network to obtain a more accurate recognition result.
Preferably, the language models in the language model database are set based on teacher and student speech characteristics and teaching content.
The invention also provides a teaching interaction intelligent education platform interaction method, which is applied to an interaction system of the teaching interaction intelligent education platform, and comprises the following steps:
s1, the teacher logs in the intelligent education platform through the teacher end, selects teaching contents, and calls a corresponding neural network model for voice recognition according to the identity information of the teacher and the selected teaching contents; a plurality of students log in the intelligent education platform through student terminals, obtain teaching contents selected by teachers, and call corresponding neural network models for voice recognition according to the identities of the students and the teaching contents selected by the teachers;
and S2, initializing display interfaces of the teacher-side and student-side intelligent education platforms, and respectively acquiring voice input and video input of the teacher side and the student side. The display interface of the intelligent education platform at least comprises a teaching process recording area;
s3: acquiring operation instructions sent by a teacher end and a student end to perform related operation, and simultaneously, rapidly recognizing the received voice information by the first voice recognition modules of the teacher end and the student end and converting the voice information into text information;
s4: displaying an operation instruction sent by a teacher end and an operation instruction sent by a student end and text information corresponding to voice information obtained by the teacher end and the student end in teaching process recording areas of the teacher end and the student end, and marking the number or name of the teacher end or the student end corresponding to each piece of text information in front of each piece of text information;
s5: a teacher obtains student-side interactive information in a teaching process, calibrates the interactive information according to text information displayed in a teaching process recording area, and performs classroom teaching according to the interactive information; the students perform auxiliary identification on the interactive information obtained by the teacher end through the text information displayed in the teaching process recording area;
s6: and the second voice recognition module is used for accurately recognizing the voice of the user and correcting the text information obtained by quick recognition.
S7: after the teaching is finished, uploading a teaching screen recording video and a teaching process record, and storing the teaching screen recording video and the teaching process record in a storage module of the intelligent education platform.
Preferably, the display interface of the intelligent education platform further comprises a video image display area, wherein the video image display area at the teacher end simultaneously displays video images acquired by a plurality of students.
Preferably, the student interaction information includes voice information and video information acquired by the student side.
Preferably, the first speech recognition module adopts speech recognition based on a unidirectional recurrent neural network to obtain a real-time recognition result; the second voice recognition module adopts voice recognition based on a bidirectional cyclic neural network to obtain a more accurate recognition result.
Preferably, the step 4 teaching process record specifically includes the following steps:
s401: and identifying whether the teacher-side voice information is an interrogative sentence and requiring the student to answer, if so, performing step S402, and otherwise, performing step S403.
S402: and acquiring voice information of the students, converting the voice information into text information, judging whether the text information is answer information of the students, if so, displaying the text information below a problem provided by a teacher in a first line indentation mode, and if not, recording the text information in a non-first line indentation mode.
S403: and continuously acquiring the voice information of the teacher end and the student end.
Preferably, the correction of the text information of step S6 further includes the steps of:
s601: comparing the text data identified by the second voice identification module with the text data identified by the first voice identification module sentence by sentence, and taking the text data as teaching process recording data when the comparison result is consistent; and when the comparison result of the two is inconsistent, the recognition text data of the second voice recognition module is used as teaching process recording data, and the data is labeled.
S602: manually verifying the marked teaching process record data, and if the recognition result of the marked teaching process data is correct, training the first voice recognition module by taking the section of voice as sample data again; and if the recognition result of the marked teaching process data is wrong, modifying the marked teaching process data, and training the first voice recognition module and the second voice recognition module by using the voice data as sample data.
Preferably, the language model in the language model database used for training the neural network model is set based on teacher and student speech characteristics and teaching content.
Compared with the prior art, the invention has the beneficial effects that:
(1) through the detailed recording of the interactive content between the teacher and the student in the teaching process recording area, the voice content between the teacher and the student can be known by the other side, the text interaction is realized while the voice interaction is carried out, the problem of unsmooth interaction caused by network fluctuation is avoided, and the teaching experience is improved.
(2) In the teaching process recording area, the student end number or the student name corresponding to each piece of text information is marked before each piece of text information, so that the situation that when a teacher receives voice interaction information from a plurality of students, the source of the interaction information is unclear, and the students cannot be subjected to targeted teaching is avoided.
(3) The recurrent neural network algorithm is improved, the calculation process is simplified by adding a simplified denoising process, and meanwhile, the accuracy of voice recognition is improved.
(4) The instantaneity of voice recognition is realized through the arrangement of the voice recognition module 1, and the transmission of interactive information is ensured to be in place; the accuracy of the teaching process record is guaranteed through the arrangement of the voice recognition module 2, students can conveniently learn the learning video after class, and teachers can conveniently analyze the learning process, so that the teaching effect is improved.
(5) The voice characteristics of teachers and students and the teaching contents are used as language models, so that the training time of the recurrent neural network is shortened, and higher voice recognition accuracy is realized for the teachers, the students and the teaching contents.
Drawings
FIG. 1 is a diagram of an interactive system of the intelligent education platform for teaching interaction according to the present invention;
FIG. 2 is a flow chart of the interactive intelligent education platform method of teaching interaction of the present invention;
FIG. 3 is a block diagram of a teacher/student end of the present invention;
FIG. 4 is a flow chart of speech recognition according to the present invention;
FIG. 5 is a schematic view of a recording interface for teaching process according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to fig. 1-3, an interactive system of a smart education platform for teaching interaction is characterized by comprising a smart education platform, a teacher end and a plurality of student ends, wherein the smart education platform is in communication connection with the teacher end and the plurality of student ends through a network;
the intelligent education platform comprises a login module, a language model database and a data processing module;
the teacher side includes: the display module comprises a teaching process recording area for displaying teaching records; the audio output module is used for outputting the received voice information; the voice input module is used for receiving voice information of a teacher end; the video input module is used for shooting the teaching process of a teacher; the first voice recognition module is used for quickly recognizing the voice information of the user and converting the voice information into text information; the second voice recognition module is used for accurately recognizing the voice information of the user and correcting the text information obtained by quick recognition;
the student terminal includes: the display module comprises a teaching process recording area for displaying teaching records; the audio output module is used for outputting the received voice information; the voice input module is used for receiving the voice information of the student end; the video input module is used for shooting the learning process of students; the first voice recognition module is used for quickly recognizing the voice information of the user and converting the voice information into text information; and the second voice recognition module is used for accurately recognizing the voice information of the user and correcting the text information obtained by quick recognition.
Referring to fig. 4, the voice recognition process of the present invention is as follows, first, recording voices of teachers and students as voice samples, and performing model training in combination with teaching contents to obtain trained classifiers. In the voice recognition process, the received input voice information is subjected to feature picking to obtain a serialized feature value matrix, the serialized feature value matrix is input into a neural network classifier to be operated, and finally, a text corresponding to the voice is obtained and output.
The invention collects the voices of different teachers and students, takes the voice segments with the pronunciation characteristics of the users as training samples of the voice recognition neural network, and establishes a language model database which is exclusive for different teachers and students by combining the teaching content, thereby greatly reducing the training cost of the voice recognition neural network and effectively improving the accuracy of the voice recognition of the neural network model aiming at the corresponding teachers, students and teaching models.
The first voice recognition module adopts voice recognition based on a unidirectional circulation neural network to obtain a recognition result in real time, and the recognition result is immediately reflected in a teaching process recording area of the terminal to assist teachers and students in timely obtaining interactive voice content and learning process records.
The second voice recognition module adopts voice recognition based on a bidirectional cyclic neural network to obtain a more accurate recognition result, and the second voice recognition module also recognizes the voice data while the first voice recognition module recognizes the voice data. And correcting the teaching process record according to the recognition result obtained by the second voice recognition module so as to obtain an accurate teaching process record.
The specific scheme is as follows: comparing the text data recognized by the second voice recognition module with the text data recognized by the first voice recognition module sentence by sentence, and taking the text data as teaching process record data when the comparison result of the text data and the text data is consistent; and when the comparison result of the two is inconsistent, the recognition text data of the second voice recognition module is used as teaching process recording data, and the data is labeled.
Manually verifying the marked teaching process record data, and if the recognition result of the marked teaching process data is correct, training the first voice recognition module by taking the section of voice as sample data again; and if the recognition result of the labeled teaching process data is wrong, modifying the labeled teaching process data, and training the first voice recognition module and the second voice recognition module by taking the section of voice data as sample data.
Through the steps, the classifier of the neural network can be perfected while accurate teaching process records are obtained, so that the method can be more suitable for the pronunciation habits of users.
Further, the language models in the language model database are set based on teacher and student speech characteristics and teaching content.
Referring to fig. 5, the teaching process record interface of the present application records at least the following: instructions sent by the teacher end and the student end, voice information sent by the teacher end and the student end, time information of the operation, sources corresponding to the voice information, real-time video images in the teaching process, classroom analysis information after teaching is finished, and after-class learning task information arranged by the teacher.
Example two:
referring to fig. 2, the method for interacting a smart education platform for teaching interaction is applied to an interaction system of a smart education platform for teaching interaction, and includes the following steps:
s1, the teacher logs in the intelligent education platform through the teacher end, selects teaching contents, and calls a corresponding neural network model for voice recognition according to the identity information of the teacher and the selected teaching contents; a plurality of students log in the intelligent education platform through student terminals, obtain teaching contents selected by teachers, and call corresponding neural network models for voice recognition according to the identities of the students and the teaching contents selected by the teachers;
and S2, initializing display interfaces of the teacher-side and student-side intelligent education platforms, and respectively acquiring voice input and video input of the teacher side and the student side. The display interface of the intelligent education platform at least comprises a teaching process recording area;
s3: acquiring operation instructions sent by a teacher end and a student end to perform related operations, and simultaneously, rapidly recognizing the received voice information by the first voice recognition modules of the teacher end and the student end and converting the voice information into text information;
s4: displaying an operation instruction sent by a teacher end and an operation instruction sent by a student end and text information corresponding to voice information obtained by the teacher end and the student end in teaching process recording areas of the teacher end and the student end, and marking the number or name of the teacher end or the student end corresponding to each piece of text information in front of each piece of text information;
s5: a teacher obtains student-side interactive information in a teaching process, calibrates the interactive information according to text information displayed in a teaching process recording area, and performs classroom teaching according to the interactive information; the students perform auxiliary identification on the interactive information obtained by the teacher end through the text information displayed in the teaching process recording area;
s6: and the second voice recognition module is used for accurately recognizing the voice of the user and correcting the text information obtained by quick recognition.
S7: after the teaching is finished, uploading a teaching screen recording video and a teaching process record, and storing the teaching screen recording video and the teaching process record in a storage module of the intelligent education platform.
The teaching process record of the step 4 specifically comprises the following steps:
s401: and identifying whether the teacher-side voice information is an interrogative sentence and requiring the student to answer, if so, performing step S402, and otherwise, performing step S403.
S402: and acquiring voice information of the students, converting the voice information into text information, judging whether the text information is answer information of the students, if so, displaying the text information below a problem provided by a teacher in a first line indentation mode, and if not, recording the text information in a non-first line indentation mode.
S403: and continuously acquiring the voice information of the teacher end and the student end.
Therefore, the teacher and the students can know the content of the questions and the answering information of the teacher at a glance in the course of lessons, and the answering information received in a delayed way can be intensively placed under the record of the questions asked by the teacher.
The correction of the text information of step S6 further includes the steps of:
s601: comparing the text data recognized by the second voice recognition module with the text data recognized by the first voice recognition module sentence by sentence, and taking the text data as teaching process record data when the comparison result of the text data and the text data is consistent; and when the comparison result of the two is inconsistent, the recognition text data of the second voice recognition module is used as teaching process recording data, and the data is labeled.
S602: manually verifying the marked teaching process record data, and if the recognition result of the marked teaching process data is correct, training the first voice recognition module by taking the section of voice as sample data again; and if the recognition result of the marked teaching process data is wrong, modifying the marked teaching process data, and training the first voice recognition module and the second voice recognition module by using the voice data as sample data.
Therefore, the classifier for speech recognition can be continuously trained in the teaching process, so that the classifier is more suitable for the speech habit of the user and the accuracy of speech recognition is improved.
Example three:
usually, the voice data is a section of sound wave signal, and a serialized eigenvalue matrix is obtained by extracting characteristic parameters. On the basis of which classifiers required for speech recognition are established. And the computer compares the speech model stored in the computer with the characteristics of the input speech signal according to the classifier of the speech recognition in the recognition process, and finds out a series of optimal models matched with the input speech according to a certain search and matching strategy. Then, according to the definition of the model, the identification result of the computer can be given through a table look-up.
In general, the eigenvalue matrix is directly input into the neural network as input data, which results in complexity of operation and increase of operation time, and cannot meet the recognition speed required in the teaching process, thereby affecting fluency of the teaching process.
Compared with the existing recurrent neural network, the recurrent neural network adopted by the invention is improved so as to improve the identification speed and the identification precision. The specific improved architecture is as follows:
Y t ＝Softmax[V·(tanh(U·x t +W·s t-1 +ba))+by]
x t ＝FFt -1 [||FFt(x t )| 2 -|FFt(h t )| 2 | 1/2 ]
wherein s is t Is a hidden layer state vector; tanh is the activation function of the neural network, U, V, W is the shared parameter matrix, x t As a sound signal, h t Is a noise signal, ba, by are deviation values, x t For de-noised sound signals, Y t For text data, t is a feature sequence.
Wherein h is t The common noise signals picked up in the teaching environment of the teacher and the teaching environment of the students. Noise signal collection is carried out on learning and teaching environments of different students and teachers in daily life, and voice distortion caused by inconsistency of noise data in a denoising process can be effectively avoided. The denoising method can simplify the denoising process, reduce the time of speech denoising when the effective speech data is highlighted and the time of neural network operation is reduced, and compared with the method of directly guiding the speech data with noise into the neural network for recognition, the method saves the time and improves the recognition accuracy.
The above description is only exemplary of the present invention and should not be taken as limiting, and any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An interactive system of an intelligent education platform for teaching interaction is characterized by comprising the intelligent education platform, a teacher end and a plurality of student ends, wherein the intelligent education platform is in communication connection with the teacher end and the student ends through a network;
the intelligent education platform comprises a login module, a language model database and a data processing module;
the teacher side includes: the display module and the audio output module; the voice input module is used for receiving voice information of a teacher end; the video input module is used for shooting the teaching process of a teacher; the first voice recognition module is used for quickly recognizing the voice information of the user and converting the voice information into text information; the second voice recognition module is used for accurately recognizing the voice information of the user and correcting the text information obtained by quick recognition;
the student terminal includes: the display module and the audio output module; the voice input module is used for receiving the voice information of the student end; the video input module is used for shooting the learning process of students; the first voice recognition module is used for quickly recognizing the voice information of the user and converting the voice information into text information; and the second voice recognition module is used for accurately recognizing the voice information of the user and correcting the text information obtained by quick recognition.
2. The interactive system of intelligent education platform for educational interaction according to claim 1, wherein the first speech recognition module employs speech recognition based on one-way recurrent neural network to obtain the recognition result in fast real-time; the second voice recognition module adopts voice recognition based on a bidirectional recurrent neural network so as to accurately obtain a recognition result.
3. An interactive system for an intelligent educational platform for educational interaction according to claims 2-3, wherein the recurrent neural network is configured as:
Y t ＝Softmax[V·(tanh(U·x t +W·s t-1 +ba))+by]
x t ＝FFt -1 [||FFt(x t )| 2 -|FFt(h t )| 2 | 1/2 ]
wherein s is t Is a hidden layer state vector; tanh is the activation function of the neural network, U, V, W is the shared parameter matrix, x t As a sound signal, h t Is a noise signal, ba, by are deviation values, x t For de-noised sound signals, Y t For text data, t is a feature sequence.
4. The interactive system for an intelligent educational platform for instructional interaction of claim 1, wherein the language models in the language model database are based on teacher and student speech characteristics and teaching content settings.
5. An interactive method of intelligent education platform for teaching interaction, which is used in the interactive system of intelligent education platform for teaching interaction as claimed in any one of claims 1-4, comprising the steps of:
s1: a teacher logs in the intelligent education platform through a teacher end, selects teaching contents, and calls a corresponding neural network model for voice recognition according to identity information of the teacher and the selected teaching contents; a plurality of students log in the intelligent education platform through student terminals, obtain teaching contents selected by teachers, and call corresponding neural network models for voice recognition according to the identities of the students and the teaching contents selected by the teachers;
s2: initializing display interfaces of a teacher-side and a student-side intelligent education platform, and respectively acquiring voice input and video input of the teacher-side and the student-side, wherein the display interfaces of the intelligent education platform at least comprise a teaching process recording area;
s3: acquiring operation instructions sent by a teacher end and a student end to perform related operations, and simultaneously, rapidly recognizing the received voice information by the first voice recognition modules of the teacher end and the student end and converting the voice information into text information;
s4: displaying an operation instruction sent by a teacher end and an operation instruction sent by a student end and text information corresponding to voice information obtained by the teacher end and the student end in teaching process recording areas of the teacher end and the student end, and marking the number or name of the teacher end or the student end corresponding to each piece of text information in front of each piece of text information;
s5: a teacher obtains student-side interactive information in a teaching process, calibrates the interactive information according to text information displayed in a teaching process recording area, and performs classroom teaching according to the interactive information; the students perform auxiliary identification on the interactive information obtained by the teacher end through the text information displayed in the teaching process recording area;
s6: and the second voice recognition module is used for accurately recognizing the voice of the user and correcting the text information obtained by quick recognition.
S7: after the teaching is finished, uploading a teaching screen recording video and a teaching process record, and storing the teaching screen recording video and the teaching process record in a storage module of the intelligent education platform.
6. The intelligent education platform interaction method of claim 5 wherein the student interaction information includes voice information and video information obtained by the student.
7. The interactive intelligent education platform method of claim 6 wherein the step 4 recording the teaching process further includes the steps of:
s401: and identifying whether the teacher-side voice information is an interrogative sentence and requiring the student to answer, if so, performing step S402, and otherwise, performing step S403.
S402: and acquiring voice information of the students, converting the voice information into text information, judging whether the text information is answer information of the students, if so, displaying the text information below a problem provided by a teacher in a first line indentation mode, and if not, recording the text information in a non-first line indentation mode.
S403: and continuously acquiring the voice information of the teacher end and the student end.
8. The intelligent education platform interaction method of claim 6 wherein the first speech recognition module employs speech recognition based on one-way recurrent neural network to obtain real-time recognition results; the second voice recognition module adopts voice recognition based on a bidirectional cyclic neural network to obtain a more accurate recognition result.
9. The interactive intelligent education platform method of claim 6 wherein the step 6 of correcting the text message further comprises:
s601: comparing the text data recognized by the second voice recognition module with the text data recognized by the first voice recognition module sentence by sentence, and taking the text data as final teaching process record data when the comparison result of the text data and the text data is consistent; and when the comparison result of the two is inconsistent, the recognition text data of the second voice recognition module is used as the final teaching process recording data, and the data is labeled.
S602: manually verifying the marked teaching process record data, and if the identification result of the marked final teaching process data is correct, training the first voice identification module by taking the section of voice as sample data again; and if the recognition result of the marked teaching process data is wrong, modifying the marked teaching process data, and training the first voice recognition module and the second voice recognition module by using the voice data as sample data.
10. The intelligent education platform interaction method of claim 6 wherein the neural network model training uses language models in the language model database based on teacher and student speech characteristics and teaching content settings.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112950424A (en) * | 2021-03-04 | 2021-06-11 | 深圳市鹰硕技术有限公司 | Online education interaction method and device |
| CN116739859A (en) * | 2023-08-15 | 2023-09-12 | 创而新(北京)教育科技有限公司 | Method and system for on-line teaching question-answering interaction |
| CN116800919A (en) * | 2023-06-21 | 2023-09-22 | 深圳市翰视科技有限公司 | Intelligent touch screen interaction teaching equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112950424A (en) * | 2021-03-04 | 2021-06-11 | 深圳市鹰硕技术有限公司 | Online education interaction method and device |
| CN112950424B (en) * | 2021-03-04 | 2023-12-19 | 深圳市鹰硕技术有限公司 | Online education interaction method and device |
| CN116800919A (en) * | 2023-06-21 | 2023-09-22 | 深圳市翰视科技有限公司 | Intelligent touch screen interaction teaching equipment |
| CN116739859A (en) * | 2023-08-15 | 2023-09-12 | 创而新(北京)教育科技有限公司 | Method and system for on-line teaching question-answering interaction |
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