Offline intelligent voice control method and device, electronic equipment and storage medium

Abstract

The invention belongs to the technical field of voice control, and particularly discloses an off-line intelligent voice control method and device, electronic equipment and a storage medium, and the method comprises the steps: enabling the outputs of a plurality of preset voice recognition models to be connected through a twin tag auxiliary module improved for a voice recognition task, performing fine tuning training on the plurality of connected preset speech recognition models to obtain a trained speech recognition model; receiving a target voice signal, inputting the target voice signal into the trained voice recognition model, and obtaining a voice recognition result output by the voice recognition model; and initiating control to corresponding equipment based on the voice recognition result. And the safety and the portability of voice control can be improved.

Description

Technical Field

[0001] The present application belongs to the field of voice control technology, and more specifically, relates to an offline intelligent voice control method, device, electronic device and storage medium. Background Art

[0002] At present, intelligent voice control has made great progress, but most of the current popular voice control devices are based on large models, cannot run on mobile terminals, and need to connect to the server through the international Internet for voice recognition operations, which brings possible privacy leaks and security issues, and has poor portability. The existing voice recognition models cannot take into account both the model parameter size and recognition accuracy at the same time, and therefore cannot meet the needs of mobile terminals. Summary of the invention

[0003] In view of the defects of the prior art, the purpose of this application is to provide an offline intelligent voice control method and device, aiming to solve the problems of poor security and portability caused by the inability to run on a mobile terminal and the need to connect to the Internet.

[0004] To achieve the above objectives, in a first aspect, the present application provides an offline intelligent voice control method, comprising: The outputs of multiple preset speech recognition models are connected using a twin label auxiliary module improved for speech recognition tasks, and the connected multiple preset speech recognition models are fine-tuned to obtain a trained speech recognition model; Receiving a target speech signal, and inputting the target speech signal into the trained speech recognition model to obtain a speech recognition result output by the speech recognition model; Based on the voice recognition result, control is initiated to the corresponding device.

[0005] Based on the existing speech recognition model, the present application connects the outputs of multiple speech recognition models using a twin label auxiliary module improved for speech recognition tasks during the fine-tuning training stage, and then conducts training so that the model can obtain the outputs of other models as auxiliary information. This increases the recognition accuracy of the model in speech recognition, making the model more suitable for intelligent voice control tasks. At the same time, the model has a smaller number of model parameters, so that the model can run on smaller portable devices without connecting to the network, thereby increasing the security and portability of voice control.

[0006] According to an offline intelligent voice control method provided by the present invention, the fine-tuning training of multiple preset voice recognition models connected by twin label auxiliary modules improved for voice recognition tasks includes: Get imperative speech-text samples; The command speech-text samples are used to perform fine-tuning training on multiple preset speech recognition models connected using a twin label auxiliary module improved for speech recognition tasks.

[0007] This application uses the collected imperative speech-text samples for pre-training during the fine-tuning training stage, thereby increasing the accuracy of the model in imperative speech recognition and making the model more suitable for intelligent voice control tasks.

[0008] According to an offline intelligent voice control method provided by the present invention, after obtaining the imperative voice-text sample, the method further includes: Performing noise reduction, filtering and gain control on the speech samples in the imperative speech-text samples; Performing word2vector processing on the text sample in the imperative speech-text sample; The imperative speech-text sample is enhanced using a data enhancement method.

[0009] This application processes voice and text samples separately, and increases the sample size through data enhancement methods to improve the accuracy of model training.

[0010] According to an offline intelligent voice control method provided by the present invention, the step of inputting the target voice signal into the trained voice recognition model to obtain the voice recognition result output by the voice recognition model comprises: Inputting the target speech signal into the trained speech recognition model to obtain text information output by the speech recognition model; The key information in the text information is extracted through a keyword matching algorithm as the speech recognition result.

[0011] According to an offline intelligent voice control method provided by the present invention, the method further includes: Establishing a correspondence between preset text and control interfaces and parameters of corresponding devices; Based on the corresponding relationship, a configuration file is established.

[0012] This application configures the voice control interface by using a configuration file, which is simple and convenient, does not limit the type of controlled device, and makes the application scenarios more flexible and varied.

[0013] According to an offline intelligent voice control method provided by the present invention, initiating control to a corresponding device based on the voice recognition result includes: Based on the speech recognition result, obtaining a configuration file corresponding to the corresponding device; Based on the control interface and parameters in the configuration file, control is initiated to the corresponding device.

[0014] In a second aspect, the present application provides an offline intelligent voice control device, comprising: A training module, used to connect the outputs of multiple preset speech recognition models using a twin label auxiliary module improved for speech recognition tasks, and to perform fine-tuning training on the connected multiple preset speech recognition models to obtain a trained speech recognition model; An acquisition module is used to receive a target speech signal, and input the target speech signal into the trained speech recognition model to obtain a speech recognition result output by the speech recognition model; A control module is used to initiate control to a corresponding device based on the speech recognition result.

[0015] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the offline intelligent voice control method described in the first aspect or any possible implementation of the first aspect.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the offline intelligent voice control method described in the first aspect or any possible implementation of the first aspect.

[0017] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the offline intelligent voice control method described in the first aspect or any possible implementation of the first aspect.

[0018] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0019] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art: (1) Based on the existing speech recognition model, during the fine-tuning training stage, the outputs of multiple speech recognition models are connected using a twin label auxiliary module improved for speech recognition tasks, and then trained so that the model can obtain the outputs of other models as auxiliary information. This increases the recognition accuracy of the model in speech recognition, making the model more suitable for intelligent voice control tasks. At the same time, the model has a smaller number of model parameters, so that the model can run on smaller portable devices without connecting to the network, thereby increasing the security and portability of voice control.

[0020] (2) During the fine-tuning training phase, the collected imperative speech-text samples are used for pre-training, thereby increasing the accuracy of the model in imperative speech recognition and making the model more suitable for intelligent voice control tasks.

[0021] (3) The voice control interface can be configured by using a configuration file, which is simple and convenient. It does not limit the type of controlled device, making the application scenarios more flexible and varied. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 It is a flowchart of the offline intelligent voice control method provided in the embodiment of the present application; Figure 2 It is a schematic diagram of a speech recognition fine-tuning training model provided in an embodiment of the present application, to which a twin label auxiliary module improved for speech recognition tasks is added; Figure 3 is a structural diagram of an offline intelligent voice control device provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0026] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0027] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0028] First, the following contents are introduced: Speech recognition is one of the core technologies of intelligent voice control. Thanks to the development of machine learning and deep learning technologies, speech recognition technology has made significant breakthroughs. At present, advanced speech recognition systems can already achieve high-precision speech-to-text functions, providing a reliable foundation for intelligent voice control.

[0029] Voice signal processing is another key technology in intelligent voice control. After collecting the user's voice signal, it needs to go through a series of signal processing processes, including noise reduction, filtering, gain control, etc., to improve the quality of the voice signal. These processing technologies can effectively remove background noise and interference, making subsequent voice recognition and semantic understanding more accurate.

[0030] Natural language understanding is an important part of realizing user intention recognition in intelligent voice control. Through natural language processing technology, the system can convert the recognized text or command into a structured semantic representation, thereby understanding the user's intention and specific requirements. This enables the intelligent voice control system to respond to user instructions more intelligently and provide more personalized services.

[0031] Automatic control technology mainly uses automatic control technology based on intelligent voice to control the controlled devices through open interfaces, including obtaining and analyzing device information through open interfaces, and sending subsequent control commands.

[0032] Next, combine Figure 1-Figure 2 The offline intelligent voice control method provided in the embodiment of the present application is introduced.

[0033] Figure 1 is a flow chart of the offline intelligent voice control method provided by the embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps: Step 100, connecting the outputs of multiple preset speech recognition models, and fine-tuning the multiple preset speech recognition models connected using a twin label auxiliary module improved for speech recognition tasks to obtain a trained speech recognition model; Optionally, the preset speech recognition model can be various common speech recognition models, such as the VOSK open source speech recognition model, or it can be a convolutional neural network with various structures designed according to specific circumstances, which is not limited in this application.

[0034] Optionally, the outputs of multiple preset speech recognition models can be connected through a twin label auxiliary module improved for speech recognition tasks. The twin label auxiliary module is used to fine-tune the preset speech recognition model. It can combine multiple independent speech recognition models and improve the overall recognition performance in a specific way. It can improve the speech recognition performance of the model without increasing the model parameters, which can effectively reduce the required computing resources while improving the recognition accuracy.

[0035] Figure 2 is a schematic diagram of a speech recognition fine-tuning training model provided in an embodiment of the present application with a twin label auxiliary module improved for speech recognition tasks, such as Figure 2 As shown in the figure, MODEL1 and MODEL2 are two identical speech recognition models without the output layer, and their model parameters are independent of each other. The twin label auxiliary module improved for the speech recognition task is an auxiliary training module for fine-tuning, which connects the outputs of MODEL1 and MODEL2 so that the models can assist each other during the training process.

[0036] Output of MODEL1 model , the output of MODEL2 model , x is the model input, that is, the speech sample. Different from the ordinary twin label auxiliary module, since the output dimension of the speech model is N×T and the loss function used in fine-tuning is the CTC loss function, the twin label auxiliary module improved for speech recognition tasks improves the CTC loss. Output of the twin label auxiliary module improved for speech recognition tasks for:

[0037] The loss calculation process after the improved twin label auxiliary module is as follows:

[0038]

[0039]

[0040]

[0041]

[0042] in are all the selected dynamic programming paths when calculating CTC loss, All paths One of the paths in is the tth node on the path, is the distribution probability output by the improved twin label auxiliary module at this node. The loss function used in training is sila_ctc_loss.

[0043] After completing the training, remove the twin label auxiliary module used for fine-tuning the training, select a suitable speech recognition model based on the actual hardware computing resources and classification accuracy requirements, and use it in the actual speech recognition task to obtain the fine-tuned speech recognition model. This model is the speech recognition model actually used.

[0044] The fine-tuned speech recognition model has smaller parameters and can be run on a central processing unit (CPU). Commonly used mobile device configurations can meet the operating requirements, and the voice control device constructed by this model can be run on a mobile portable device.

[0045] In addition, since the model runs entirely on mobile portable devices, there is no need to connect to the Internet, which meets the offline operation requirements of the voice control system and improves security.

[0046] Step 110, receiving a target speech signal, and inputting the target speech signal into a trained speech recognition model to obtain a speech recognition result output by the speech recognition model; Optionally, the target voice signal can be received by a voice collection device such as a microphone, and the collected signal can be subjected to noise reduction, filtering, gain control, etc., and the voice quality can be improved through a series of preprocessing.

[0047] The collected speech signal is then input into the speech recognition model for speech recognition to obtain speech recognition results corresponding to the speech, such as text information.

[0048] Step 120: Initiate control to the corresponding device based on the voice recognition result.

[0049] After obtaining the speech recognition result, a control signal can be sent to the corresponding device to complete the device control process. If the corresponding control command is to obtain device status information, the returned device status information is received after sending the control command.

[0050] Optionally, a language broadcast module may be used to broadcast the sending status of the device control information, and for a control command for obtaining device status information, the returned device status result also needs to be broadcast.

[0051] The present application provides an offline intelligent voice control method. Based on the existing voice recognition model, in the fine-tuning training stage, the outputs of multiple voice recognition models are connected using a twin label auxiliary module improved for voice recognition tasks, and then training is performed so that the model can obtain the outputs of other models as auxiliary information, thereby increasing the recognition accuracy of the model in voice recognition, making the model more suitable for intelligent voice control tasks, and at the same time making the model have a smaller model parameter amount, so that the model can run on a smaller portable device without connecting to the network, thereby increasing the security and portability of voice control.

[0052] In some embodiments, step 100 specifically includes: Step 1001, obtaining a command voice-text sample; Step 1002, using imperative speech-text samples to fine-tune the connected multiple preset speech recognition models.

[0053] The imperative voice-text sample may be a voice sample with command meaning and its corresponding text sample, such as a voice sample and a text sample of "turn on device A".

[0054] The present application does not limit the method for obtaining the imperative voice-text samples, and the samples may be obtained through various methods such as the Internet.

[0055] After obtaining the imperative speech-text samples, the output layer of the preset speech recognition model is replaced with the twin label auxiliary module for further training. Since the twin label auxiliary module has the characteristics of improving model generalization and accelerating model convergence, a speech recognition model suitable for fine-tuning is constructed.

[0056] The speech recognition model with the output layer replaced is used to train the collected imperative speech-text samples using the gradient back-propagation algorithm, so that the training is stopped immediately after the model reaches convergence to prevent the model from overfitting on the samples.

[0057] Due to the use of the twin label auxiliary module, the model can converge faster and is less prone to overfitting, thereby obtaining a speech recognition model that is more sensitive and accurate to imperative speech.

[0058] In some embodiments, after step 1001, the method further includes: Step 10011, performing noise reduction, filtering and gain control on the speech sample in the imperative speech-text sample; Step 10012, performing word2vector processing on the text sample in the imperative speech-text sample; Step 10013, use the data enhancement method to perform data enhancement on the imperative speech-text sample.

[0059] Optionally, after obtaining the imperative speech-text samples, the speech can be denoised, filtered, and gain controlled, the text can be processed by word2vector, and then the samples can be enhanced using data enhancement methods to obtain the samples required for model fine-tuning training. By processing the speech and text samples separately and increasing the sample size through data enhancement methods, the accuracy of model training can be improved.

[0060] Optionally, the present application does not limit the specific data enhancement method used.

[0061] In some embodiments, step 110 specifically includes: Step 1101, inputting the target speech signal into the trained speech recognition model to obtain text information output by the speech recognition model; Step 1102, extracting key information from the text information through a keyword matching algorithm as a speech recognition result.

[0062] After obtaining the trained speech recognition model, the target speech signal emitted by the user can be received and input into the trained speech recognition model to obtain the text information output by the speech recognition model.

[0063] Since the text information contains key information and non-key information, the key information in the text information can be extracted through a keyword matching algorithm as a speech recognition result.

[0064] Optionally, the keyword matching algorithm may be a string matching algorithm, a fuzzy matching algorithm, etc., which is not limited in this application.

[0065] In some embodiments, the method further comprises: Step 130, establishing a correspondence between the preset text and the control interface and parameters of the corresponding device; Step 140: Create a configuration file based on the corresponding relationship.

[0066] In order not to limit the type of controlled devices and increase the usage scenarios of voice control, the voice control interface can be configured by generating a configuration file. Therefore, before using the offline intelligent voice control method provided in this application, the configuration file can be configured. First, a correspondence between the preset text and the control interface and parameters of the corresponding device is established, and then the configuration information is saved in the configuration file for subsequent use.

[0067] For example, a correspondence between the text "open device A" and the command interface and parameters of device A may be established in advance, and a corresponding configuration file may be generated.

[0068] In one embodiment of the present application, a configuration file in json format is used to save corresponding configuration information.

[0069] In some embodiments, step 120 specifically includes: Step 1201, based on the speech recognition result, obtain a configuration file corresponding to the corresponding device; Step 1202: Initiate control to the corresponding device based on the control interface and parameters in the configuration file.

[0070] After obtaining the speech recognition result, that is, recognizing the text, you can use the key information retrieval configuration in the language understanding stage to obtain the corresponding configuration file and the device interface and parameters therein, and then use the obtained control interface and parameters to initiate a control signal to the corresponding device to complete the device control process.

[0071] Figure 3 is a schematic diagram of the structure of the offline intelligent voice control device provided in the embodiment of the present application, such as Figure 3 As shown, the device includes a training module 310, an acquisition module 210 and a control module 330, wherein: A training module 310 is used to connect the outputs of multiple preset speech recognition models using a twin label auxiliary module improved for speech recognition tasks, and perform fine-tuning training on the connected multiple preset speech recognition models to obtain a trained speech recognition model; The acquisition module 320 is used to receive a target speech signal and input the target speech signal into a trained speech recognition model to obtain a speech recognition result output by the speech recognition model; The control module 330 is used to initiate control to the corresponding device based on the speech recognition result.

[0072] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0073] Based on the method in the above embodiment, Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As shown, an embodiment of the present application provides an electronic device, which may include: a processor (processor) 410, a communication interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call the logic instructions in the memory 430 to execute the offline intelligent voice control method in the above embodiment.

[0074] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the offline intelligent voice control method described in each embodiment of the present application.

[0075] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the offline intelligent voice control method in the above embodiment.

[0076] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the offline intelligent voice control method in the above embodiment.

[0077] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0078] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0079] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0080] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0081] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An offline intelligent voice control method, characterized in that: include: The outputs of multiple preset speech recognition models are connected using a twin label auxiliary module improved for speech recognition tasks, and the connected multiple preset speech recognition models are fine-tuned to obtain a trained speech recognition model; Receiving a target speech signal, and inputting the target speech signal into the trained speech recognition model to obtain a speech recognition result output by the speech recognition model; Based on the voice recognition result, control is initiated to the corresponding device.

2. The offline intelligent voice control method according to claim 1, characterized in that: The fine-tuning training of multiple preset speech recognition models connected with the twin label auxiliary modules improved for the speech recognition task includes: Get imperative speech-text samples; The command speech-text samples are used to perform fine-tuning training on multiple preset speech recognition models connected using a twin label auxiliary module improved for speech recognition tasks.

3. The offline intelligent voice control method according to claim 2, characterized in that: After obtaining the imperative speech-text sample, the method further includes: Performing noise reduction, filtering and gain control on the speech samples in the imperative speech-text samples; Performing word2vector processing on the text sample in the imperative speech-text sample; The imperative speech-text sample is enhanced using a data enhancement method.

4. The offline intelligent voice control method according to claim 1, characterized in that: The step of inputting the target speech signal into the trained speech recognition model to obtain a speech recognition result output by the speech recognition model comprises: Inputting the target speech signal into the trained speech recognition model to obtain text information output by the speech recognition model; The key information in the text information is extracted through a keyword matching algorithm as the speech recognition result.

5. The offline intelligent voice control method according to claim 1 or 4, characterized in that: The method further comprises: Establishing a correspondence between preset text and control interfaces and parameters of corresponding devices; Based on the corresponding relationship, a configuration file is established.

6. The offline intelligent voice control method according to claim 5, characterized in that: The initiating control to the corresponding device based on the speech recognition result includes: Based on the speech recognition result, obtaining a configuration file corresponding to the corresponding device; Based on the control interface and parameters in the configuration file, control is initiated to the corresponding device.

7. An offline intelligent voice control device, characterized in that: include: A training module, used to connect the outputs of multiple preset speech recognition models using a twin label auxiliary module improved for speech recognition tasks, and to perform fine-tuning training on the connected multiple preset speech recognition models to obtain a trained speech recognition model; An acquisition module is used to receive a target speech signal, and input the target speech signal into the trained speech recognition model to obtain a speech recognition result output by the speech recognition model; A control module is used to initiate control to a corresponding device based on the speech recognition result.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the offline intelligent voice control method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor executes the offline intelligent voice control method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product runs on a processor, the processor executes the offline intelligent voice control method as described in any one of claims 1-6.

Images