Multimedia electronic book control method and device for classroom teaching

Abstract

The invention provides a multimedia electronic book control method and device for classroom teaching, and the method comprises the steps: collecting the sound of a user through a microphone after the user logs in electronic book software; performing sound feature comparison on the collected user sound and teacher sound pre-stored in the multimedia electronic book to verify the identity of the user; when it is verified that the collected voice of the user is sent by the teacher as the pre-stored voice, the voice is converted into a text, and semantics of the text is recognized; converting the semantics of the recognized text into a preset operation instruction; and controlling the multimedia electronic book to execute an operation matched with the operation instruction according to the operation instruction. The method has the beneficial effects that an innovative solution is provided for intelligent control of the multimedia electronic book in classroom teaching through fusion of an intelligent voice recognition technology and a semantic analysis technology, natural and efficient interaction between a teacher and the electronic book is realized, and interactivity and efficiency of teaching are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic books, and in particular to a method and device for controlling a multimedia electronic book used for classroom teaching. Background Art

[0002] In the field of modern education, multimedia e-books have become an indispensable teaching tool in the classroom due to their rich and diverse teaching content and convenient interactive methods. With the development of intelligent voice technology, applying voice recognition technology to multimedia e-books to achieve more natural human-computer interaction is a major trend in the development of educational technology. Traditional multimedia e-book interactions mostly rely on touch screen operations and keyboard input, which are not convenient in certain specific environments (such as when teachers are teaching on the podium). Therefore, developing a technical solution that can control multimedia e-books through voice recognition and semantic analysis to improve teaching efficiency and classroom interactivity is of great significance to the development of modern teaching models. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a multimedia electronic book control method and device for classroom teaching, aiming at improving teaching efficiency.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a multimedia electronic book control method for classroom teaching, comprising:

[0005] After the user logs into the e-book software, a microphone is used to collect the user's voice;

[0006] Compare the collected user voice with the teacher's voice pre-stored in the multimedia e-book to verify the user's identity;

[0007] After verifying that the collected user voice is uttered by a teacher with a pre-stored voice, the voice is converted into text, and the semantics of the text is recognized;

[0008] Convert the semantics of the recognized text into preset operation instructions;

[0009] According to the operation instruction, the multimedia electronic book is controlled to execute the operation matching the operation instruction.

[0010] Furthermore, after the user logs into the e-book software, using a microphone to collect the user's voice includes:

[0011] Using a microphone to collect user voice and convert the user voice into a digital signal;

[0012] The user's voice converted into a digital signal is subjected to denoising and filtering.

[0013] Furthermore, the step of comparing the collected user voice with the teacher's voice pre-stored in the multimedia electronic book to verify the user's identity includes:

[0014] Use the Mel frequency cepstral coefficient feature extraction algorithm to extract feature vectors from the user's voice signal;

[0015] Matching the extracted feature vector with the sample sound feature vector pre-stored in the multimedia e-book through a dynamic time warping algorithm or a hidden Markov model algorithm;

[0016] If the match is successful, it is confirmed that the user's voice is from the teacher with the pre-stored voice.

[0017] Further, upon verifying that the collected user voice is uttered by a teacher with a pre-stored voice, converting the voice into text and identifying the semantics of the text includes:

[0018] Use automatic speech recognition models to convert teacher voice into text;

[0019] Perform word segmentation, part-of-speech tagging, and named entity recognition on the converted text;

[0020] Use a deep learning-based semantic understanding model to perform semantic analysis on the text and identify the teacher's operational intentions.

[0021] Furthermore, controlling the multimedia electronic book to perform an operation matching the operation instruction according to the operation instruction includes:

[0022] Parse the operation instructions to determine the specific operation type and operation parameters;

[0023] Check the rationality of the operation instructions according to the status of the multimedia e-book;

[0024] Convert reasonable operation instructions into virtual touch screen operation, mouse operation or keyboard input operation;

[0025] According to the generated virtual signal, the multimedia e-book gives a corresponding response.

[0026] The present invention also provides a multimedia electronic book control device for classroom teaching, comprising:

[0027] The sound collection module is used to collect the user's voice using a microphone after the user logs into the e-book software;

[0028] The identity verification module is used to compare the collected user voice with the teacher's voice pre-stored in the multimedia e-book to verify the user's identity;

[0029] The semantic recognition module is used to convert the collected user voice into text and recognize the semantics of the text after verifying that the collected user voice is uttered by the teacher with the pre-stored voice;

[0030] An instruction conversion module, used to convert the semantics of the recognized text into preset operation instructions;

[0031] The instruction execution module is used to control the multimedia electronic book to execute an operation matching the operation instruction according to the operation instruction.

[0032] Furthermore, the sound collection module is used to:

[0033] Using a microphone to collect user voice and convert the user voice into a digital signal;

[0034] The user's voice converted into a digital signal is subjected to denoising and filtering.

[0035] Furthermore, the identity verification module is specifically used for:

[0036] Use the Mel frequency cepstral coefficient feature extraction algorithm to extract feature vectors from the user's voice signal;

[0037] Matching the extracted feature vector with the sample sound feature vector pre-stored in the multimedia e-book through a dynamic time warping algorithm or a hidden Markov model algorithm;

[0038] If the match is successful, it is confirmed that the user's voice is from the teacher with the pre-stored voice.

[0039] Furthermore, the semantic recognition module is specifically used for:

[0040] Use automatic speech recognition models to convert teacher voice into text;

[0041] Perform word segmentation, part-of-speech tagging, and named entity recognition on the converted text;

[0042] Use a deep learning-based semantic understanding model to perform semantic analysis on the text and identify the teacher's operational intentions.

[0043] Furthermore, the instruction execution module is specifically used for:

[0044] Parse the operation instructions to determine the specific operation type and operation parameters;

[0045] Check the rationality of the operation instructions according to the status of the multimedia e-book;

[0046] Convert reasonable operation instructions into virtual touch screen operation, mouse operation or keyboard input operation;

[0047] According to the generated virtual signal, the multimedia e-book gives a corresponding response.

[0048] The beneficial effect of the present invention is that, through the integration of intelligent speech recognition technology and semantic analysis technology, an innovative solution is provided for the intelligent control of multimedia e-books in classroom teaching, natural and efficient interaction between teachers and e-books is achieved, and the interactivity and efficiency of teaching are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The specific structure of the present invention is described in detail below in conjunction with the accompanying drawings.

[0050] Figure 1 It is a flow chart of the multimedia electronic book control method for classroom teaching of the present invention;

[0051] Figure 2 A flowchart of user voice collection and preprocessing according to an embodiment of the present invention;

[0052] Figure 3 A flow chart of voice identity authentication according to an embodiment of the present invention;

[0053] Figure 4 This is a flow chart of speech-to-text and semantic recognition according to an embodiment of the present invention;

[0054] Figure 5 is a flowchart of the operation instruction execution of an embodiment of the present invention;

[0055] Figure 6 A block diagram of a multimedia electronic book control device for classroom teaching according to an embodiment of the present invention;

[0056] Figure 7 A schematic block diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0059] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0060] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0061] like Figure 1 As shown, the first embodiment of the present invention is: a multimedia electronic book control method for classroom teaching, comprising the steps of:

[0062] S10. After the user logs into the e-book software, a microphone is used to collect the user's voice.

[0063] like Figure 2 As shown, step S10 specifically includes:

[0064] S11, using a microphone to collect user voice and convert the user voice into a digital signal;

[0065] S12, performing denoising and filtering processing on the user voice converted into a digital signal.

[0066] In this embodiment, sound collection: when the e-book software is started, the built-in or external microphone device is automatically detected and enabled. After the user logs in to the e-book software, the user starts to collect the surrounding sound through the interface prompt or automatically. Sound signal digitization: Use an analog / digital converter (ADC) to convert the analog sound signal into a digital signal for subsequent processing. Denoising: Apply adaptive filters or wavelet transform denoising technology to eliminate non-human voice background noise. Filtering processing: Perform spectral analysis on the digital signal to remove non-speech parts, such as high-order harmonics or low-frequency items.

[0067] S20: Compare the collected user voice with the teacher's voice pre-stored in the multimedia electronic book to verify the user's identity.

[0068] like Figure 3 As shown, step S20 specifically includes:

[0069] S21, extracting a feature vector from the user voice signal using a Mel frequency cepstral coefficient feature extraction algorithm;

[0070] S22, matching the extracted feature vector with the sample sound feature vector pre-stored in the multimedia electronic book by using a dynamic time warping algorithm or a hidden Markov model algorithm;

[0071] S23. If the match is successful, it is confirmed that the user's voice is from the teacher with the pre-stored voice.

[0072] In this embodiment, feature extraction: Mel frequency cepstral coefficient (MFCC) extraction: use the Mel frequency cepstral coefficient algorithm to extract feature vectors from the user voice signal. The core of this step is to calculate the short-time Fourier transform (STFT), apply logarithmic and inverse cosine transform on the Mel scale to obtain MFCC features. Pre-enhancement: In order to improve the robustness of the feature, the feature vector is pre-enhanced, including normalization and Delta-Delta feature calculation. Feature matching: Dynamic time warping algorithm (DTW): Use the dynamic time warping algorithm to match and compare the feature vector of the user's voice with the pre-stored sample sound. The algorithm can match the feature vector even if there are changes in time by minimizing the distance between the two curves. Hidden Markov model (HMM): Or use the hidden Markov model algorithm to match the sound signal with the pre-stored model by establishing a state transition probability and observation probability model. Verification result: Once the match is successful, the system confirms that the current user's voice is emitted by the teacher of the pre-stored voice, and performs subsequent voice operations.

[0073] S30: After verifying that the collected user voice is uttered by a teacher with a pre-stored voice, convert the voice into text and identify the semantics of the text.

[0074] like Figure 4 As shown, step S30 specifically includes:

[0075] S31. Use the automatic speech recognition model to convert the teacher’s voice into text;

[0076] S32, performing word segmentation, part-of-speech tagging, and named entity recognition processing on the converted text;

[0077] S33. Use a semantic understanding model based on deep learning to perform semantic analysis on the text and identify the teacher's operational intentions.

[0078] In this embodiment, automatic speech recognition (ASR): using deep learning algorithms, such as connection temporal classification (CTC) algorithm, the user voice converted into digital signals is converted into text. A large number of teacher voice samples are used to optimize the model during training to ensure high accuracy. Text preprocessing: word segmentation: natural language processing (NLP) word segmentation is performed on the text obtained by ASR. Part-of-speech tagging: part-of-speech tagging is performed on the text after word segmentation to determine the grammatical properties of each word. Named entity recognition (NER): Identify specific entities in the text, such as names, places, organization names, etc. Semantic analysis: Use a Transformer-based deep learning model for semantic understanding. Use the teacher's teaching text to build a semantic model and analyze the teacher's operating intentions. Encode and decode the text through the attention mechanism to extract deep semantic information. According to the training and optimization of the model, the algorithm is continuously updated to increase accuracy.

[0079] S40: Convert the semantics of the recognized text into a preset operation instruction.

[0080] In this embodiment, intention understanding and instruction generation: Based on the results of semantic analysis, the teacher's operation intention is understood as a series of operation instructions. These instructions include but are not limited to "open page", "zoom in", "close window", etc. Instruction parsing: The operation instructions are parsed grammatically and semantically to determine the specific operation type and parameters. If the instruction contains a numeric parameter, numerical recognition and conversion are required.

[0081] S50: According to the operation instruction, control the multimedia electronic book to execute an operation matching the operation instruction.

[0082] like Figure 5 As shown, step S50 specifically includes:

[0083] S51, parsing the operation instruction to determine the specific operation type and operation parameters;

[0084] S52, checking the rationality of the operation instruction according to the state of the multimedia electronic book;

[0085] S53, converting the reasonable operation instruction into a virtual touch screen operation, mouse operation or keyboard input operation;

[0086] S54. According to the generated virtual signal, the multimedia electronic book gives a corresponding response.

[0087] In this embodiment, instruction rationality check: check the rationality of the operation instruction according to the current state of the multimedia e-book. If the "close" instruction is sent to a closed window, the instruction is deemed unreasonable and the system will not execute it. Operation instruction conversion: convert reasonable operation instructions into corresponding virtual touch screen operations, mouse operations or keyboard input operations. Including but not limited to simulated clicks, slides and typing operations. Response generation: according to the generated virtual signal, the multimedia e-book gives a corresponding response. Such as opening a specified page, enlarging content, closing an application, etc. Ensure the speed and accuracy of the response and provide instant feedback.

[0088] By implementing the technical solution of the present invention, it is expected that the following technical effects can be achieved:

[0089] Enhance classroom interactivity: Teachers can directly control multimedia e-books through voice without leaving the podium to operate the screen, improving interactivity.

[0090] Improve teaching efficiency: The operation process of e-books has been simplified. Teachers can directly give operation instructions through voice, reducing the time of manual operation.

[0091] Enhanced security and exclusivity: Voice authentication increases the security of e-book use and ensures teachers' exclusive control over e-books.

[0092] Improve recognition accuracy: Signal preprocessing steps such as denoising, filtering, and pre-enhancement improve the quality of speech signals and provide an accurate basis for subsequent matching and semantic analysis.

[0093] Realize intelligent control: The semantic understanding model based on deep learning can accurately analyze the teacher's intentions and convert them into e-book operation instructions to realize intelligent control.

[0094] Adapt to various teaching scenarios: This solution can adapt to different teaching environments and noise levels, ensuring that the e-book can work stably in various situations.

[0095] Optimize teacher operation experience: The interactive experience with fast response speed, high accuracy and good usability enables teachers to have a better sense of control over multimedia content in teaching.

[0096] In summary, the present invention provides an innovative solution for the intelligent control of multimedia e-books in classroom teaching through the integration of intelligent speech recognition technology and semantic analysis technology, realizes natural and efficient interaction between teachers and e-books, and improves the interactivity and efficiency of teaching.

[0097] like Figure 6 As shown, the embodiment of the present invention also provides a multimedia electronic book control device for classroom teaching, comprising:

[0098] The sound collection module 10 is used to collect the user's voice using a microphone after the user logs into the e-book software;

[0099] The identity verification module 20 is used to compare the collected user voice with the teacher's voice pre-stored in the multimedia electronic book to verify the user's identity;

[0100] The semantic recognition module 30 is used to convert the collected user voice into text and recognize the semantics of the text after verifying that the collected user voice is uttered by a teacher with a pre-stored voice;

[0101] An instruction conversion module 40, used to convert the semantics of the recognized text into a preset operation instruction;

[0102] The instruction execution module 50 is used to control the multimedia electronic book to execute an operation matching the operation instruction according to the operation instruction.

[0103] In a specific embodiment, the sound collection module 10 is used for:

[0104] Using a microphone to collect user voice and convert the user voice into a digital signal;

[0105] The user's voice converted into a digital signal is subjected to denoising and filtering.

[0106] In a specific embodiment, the identity verification module 20 is specifically used for:

[0107] Use the Mel frequency cepstral coefficient feature extraction algorithm to extract feature vectors from the user's voice signal;

[0108] Matching the extracted feature vector with the sample sound feature vector pre-stored in the multimedia e-book through a dynamic time warping algorithm or a hidden Markov model algorithm;

[0109] If the match is successful, it is confirmed that the user's voice is from the teacher with the pre-stored voice.

[0110] In a specific embodiment, the semantic recognition module 30 is specifically used for:

[0111] Use automatic speech recognition models to convert teacher voice into text;

[0112] Perform word segmentation, part-of-speech tagging, and named entity recognition on the converted text;

[0113] Use a deep learning-based semantic understanding model to perform semantic analysis on the text and identify the teacher's operational intentions.

[0114] In a specific embodiment, the instruction execution module 50 is specifically used to:

[0115] Parse the operation instructions to determine the specific operation type and operation parameters;

[0116] Check the rationality of the operation instructions according to the status of the multimedia e-book;

[0117] Convert reasonable operation instructions into virtual touch screen operation, mouse operation or keyboard input operation;

[0118] According to the generated virtual signal, the multimedia e-book gives a corresponding response.

[0119] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned multimedia e-book control device for classroom teaching can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0120] The above-mentioned multimedia electronic book control device for classroom teaching can be implemented in the form of a computer program. The computer program can be used in Figure 7 Runs on the computer device shown.

[0121] See also Figure 7 , Figure 7 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0122] See also Figure 7 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0123] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a multimedia electronic book control method for classroom teaching.

[0124] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0125] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a multimedia electronic book control method for classroom teaching.

[0126] The network interface 505 is used to communicate with other devices over the network. Figure 7 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] The processor 502 is used to run the computer program 5032 stored in the memory to implement the multimedia electronic book control method for classroom teaching as described above.

[0128] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0129] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0130] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the multimedia electronic book control method for classroom teaching as described above.

[0131] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0132] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0133] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0134] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0136] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A multimedia electronic book control method for classroom teaching, characterized in that: include: After the user logs into the e-book software, a microphone is used to collect the user's voice; Compare the collected user voice with the teacher's voice pre-stored in the multimedia e-book to verify the user's identity; After verifying that the collected user voice is uttered by a teacher with a pre-stored voice, the voice is converted into text, and the semantics of the text is recognized; Convert the semantics of the recognized text into preset operation instructions; According to the operation instruction, the multimedia electronic book is controlled to execute the operation matching the operation instruction.

2. The multimedia electronic book control method for classroom teaching as claimed in claim 1, characterized in that: After the user logs into the e-book software, using a microphone to collect the user's voice includes: Using a microphone to collect user voice and convert the user voice into a digital signal; The user's voice converted into a digital signal is subjected to denoising and filtering.

3. The multimedia electronic book control method for classroom teaching as claimed in claim 1, characterized in that: The step of comparing the collected user voice with the teacher's voice pre-stored in the multimedia electronic book to verify the user's identity includes: Use the Mel frequency cepstral coefficient feature extraction algorithm to extract feature vectors from the user's voice signal; Matching the extracted feature vector with the sample sound feature vector pre-stored in the multimedia e-book through a dynamic time warping algorithm or a hidden Markov model algorithm; If the match is successful, it is confirmed that the user's voice is from the teacher with the pre-stored voice.

4. The multimedia electronic book control method for classroom teaching as claimed in claim 1, characterized in that: The method of verifying that the collected user voice is uttered by a teacher with a pre-stored voice, converting the voice into text, and recognizing the semantics of the text includes: Use automatic speech recognition models to convert teacher voice into text; Perform word segmentation, part-of-speech tagging, and named entity recognition on the converted text; Use a deep learning-based semantic understanding model to perform semantic analysis on the text and identify the teacher's operational intentions.

5. The multimedia electronic book control method for classroom teaching as claimed in claim 1, characterized in that: According to the operation instruction, controlling the multimedia electronic book to perform an operation matching the operation instruction includes: Parse the operation instructions to determine the specific operation type and operation parameters; Check the rationality of the operation instructions according to the status of the multimedia e-book; Convert reasonable operation instructions into virtual touch screen operation, mouse operation or keyboard input operation; According to the generated virtual signal, the multimedia e-book gives a corresponding response.

6. A multimedia electronic book control device for classroom teaching, characterized in that: include: The sound collection module is used to collect the user's voice using a microphone after the user logs into the e-book software; The identity verification module is used to compare the collected user voice with the teacher's voice pre-stored in the multimedia e-book to verify the user's identity; The semantic recognition module is used to convert the collected user voice into text and recognize the semantics of the text after verifying that the collected user voice is uttered by the teacher with the pre-stored voice; An instruction conversion module, used to convert the semantics of the recognized text into preset operation instructions; The instruction execution module is used to control the multimedia electronic book to execute an operation matching the operation instruction according to the operation instruction.

7. The multimedia electronic book control device for classroom teaching as claimed in claim 6, characterized in that: The sound collection module is specifically used for: Using a microphone to collect user voice and convert the user voice into a digital signal; The user's voice converted into a digital signal is subjected to denoising and filtering.

8. The multimedia electronic book control device for classroom teaching as claimed in claim 6, characterized in that: The identity authentication module is specifically used for: Use the Mel frequency cepstral coefficient feature extraction algorithm to extract feature vectors from the user's voice signal; Matching the extracted feature vector with the sample sound feature vector pre-stored in the multimedia e-book through a dynamic time warping algorithm or a hidden Markov model algorithm; If the match is successful, it is confirmed that the user's voice is from the teacher with the pre-stored voice.

9. The multimedia electronic book control device for classroom teaching as claimed in claim 6, characterized in that: The semantic recognition module is specifically used for: Use automatic speech recognition models to convert teacher voice into text; Perform word segmentation, part-of-speech tagging, and named entity recognition on the converted text; Use a deep learning-based semantic understanding model to perform semantic analysis on the text and identify the teacher's operational intentions.

10. The multimedia electronic book control device for classroom teaching as claimed in claim 6, characterized in that: The instruction execution module is specifically used for: Parse the operation instructions to determine the specific operation type and operation parameters; Check the rationality of the operation instructions according to the status of the multimedia e-book; Convert reasonable operation instructions into virtual touch screen operation, mouse operation or keyboard input operation; According to the generated virtual signal, the multimedia e-book gives a corresponding response.

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