CN108806670A - Audio recognition method, device and storage medium - Google Patents

Abstract

The invention discloses a voice recognition method, device and storage medium, belonging to the technical field of voice processing. The method includes: collecting target speech to be recognized, and acquiring the acoustic features of the target speech. Invoke the target recognition model, input the acoustic features into the target recognition model, and output the behavioral intention label corresponding to the target voice, and the target recognition model is used to identify the behavioral intention corresponding to the voice according to the acoustic features of any voice. In the embodiment of the present invention, whether it is a standard voice or a non-standard voice, the corresponding behavioral intention can be recognized through the target recognition model based on the acoustic features, which enhances the applicability of the voice recognition.

Description

Speech recognition method, device and storage medium

technical field

Embodiments of the present invention relate to the technical field of voice processing, and in particular, to a voice recognition method, device, and storage medium.

Background technique

At present, speech recognition technology has been widely used. For example, in the process of using the terminal, the user can use the voice recognition technology to control the terminal, for example, control the terminal to turn on the camera and so on.

In related technologies, after the terminal collects the voice input by the user, it sends the voice to a voice conversion server, and the voice conversion server can convert the voice into text, and then sends the converted text to the terminal. After receiving the text, the terminal can send the text to the semantic recognition server, and the semantic recognition server performs semantic recognition on the text and feeds back the recognition result to the terminal. In this way, the terminal can perform corresponding operations based on the identification result.

However, in the above implementation process, only standard speech can be recognized, that is to say, only Mandarin Chinese can be recognized, and the applicability of speech recognition is poor.

Contents of the invention

Embodiments of the present invention provide a voice recognition method, device and storage medium, which can solve the problem of poor applicability of voice recognition in the related art. Described technical scheme is as follows:

In a first aspect, a speech recognition method is provided, the method comprising:

Collect the target voice to be recognized;

Acquiring the acoustic features of the target speech;

Invoking the target recognition model, inputting the acoustic features into the target recognition model, and outputting the behavioral intention label corresponding to the target voice, the target recognition model is used to identify the corresponding behavior of the voice according to the acoustic features of any voice behavioral intent.

Optionally, before calling the target recognition model, it also includes:

Acquiring the acoustic features of at least one speech training sample and the behavioral intent label corresponding to each speech training sample;

Based on the acoustic feature of the at least one speech training sample and the behavioral intention label corresponding to each speech training sample, the recognition model to be trained is trained to obtain the target recognition model.

Optionally, obtain the behavioral intent label corresponding to each voice training sample, including:

Get at least one voice;

determining the behavior operation corresponding to each voice in the at least one voice;

Generate behavioral intent tags corresponding to each behavioral operation;

Determining the at least one speech as the at least one speech training sample, and determining each generated behavioral intention label as the behavioral intention label of the corresponding speech training sample.

Optionally, before acquiring at least one voice, it also includes:

According to the voiceprint feature of each voice, inquire whether the at least one voice is from a target user, where the target user refers to a user associated with the first terminal;

When the at least one voice is from the target user, the operation of acquiring at least one voice is performed.

Optionally, the querying whether the at least one voice is from the target user according to the voiceprint feature of each voice includes:

Determine the difference between the voiceprint feature of each voice and the preset voiceprint feature;

When the difference values between the voiceprint feature of each voice and the preset voiceprint feature are smaller than a preset threshold, it is determined that the at least one voice is from the target user.

Optionally, the training of the recognition model to be trained based on the acoustic features of the at least one speech training sample and the behavioral intention label corresponding to each speech training sample, after obtaining the target recognition model, further includes:

The target recognition model is shared with a second terminal, where the second terminal refers to a terminal that has an association relationship with the first terminal.

In a second aspect, a speech recognition device is provided, the device comprising:

The collection module is used to collect the target voice to be recognized;

A first acquisition module, configured to acquire the acoustic features of the target speech;

The calling module is used to call the target recognition model, input the acoustic features into the target recognition model, and output the behavioral intention label corresponding to the target voice, and the target recognition model is used to recognize any voice according to the acoustic features The behavioral intention corresponding to the voice.

Optionally, the device also includes:

The second obtaining module is used to obtain the acoustic features of at least one speech training sample and the behavioral intention label corresponding to each speech training sample;

A training module, configured to train a recognition model to be trained based on the acoustic features of the at least one speech training sample and the behavioral intention label corresponding to each speech training sample, to obtain the target recognition model.

Optionally, the second acquisition module is used for:

Get at least one voice;

determining the behavior operation corresponding to each voice in the at least one voice;

Generate behavioral intent tags corresponding to each behavioral operation;

Determining the at least one speech as the at least one speech training sample, and determining each generated behavioral intention label as the behavioral intention label of the corresponding speech training sample.

Optionally, the second acquisition module is also used for:

According to the voiceprint feature of each voice, inquire whether the at least one voice is from a target user, where the target user refers to a user associated with the first terminal;

When the at least one voice is from the target user, the operation of acquiring at least one voice is performed.

Optionally, the second acquisition module is also used for:

Determine the difference between the voiceprint feature of each voice and the preset voiceprint feature;

When the difference values between the voiceprint feature of each voice and the preset voiceprint feature are smaller than a preset threshold, it is determined that the at least one voice is from the target user.

Optionally, the device also includes:

A sharing module, configured to share the target recognition model with a second terminal, where the second terminal refers to a terminal that has an association relationship with the first terminal.

In a third aspect, a computer-readable storage medium is provided, where instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the speech recognition method described in the above-mentioned first aspect is implemented.

In a fourth aspect, there is provided a computer program product containing instructions, which, when run on a computer, causes the computer to execute the speech recognition method described in the first aspect above.

In a fifth aspect, a computing device is provided, including:

one or more processors;

memory for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors are made to execute the voice recognition method described in the first aspect above.

The beneficial effects brought by the technical solution provided by the embodiments of the present invention are:

Collect the target speech to be recognized, and obtain the acoustic features of the target speech. Call the target recognition model, because the target recognition model can recognize the behavioral intention corresponding to the voice according to the acoustic features of any voice, so after inputting the acquired acoustic features into the target recognition model, the behavior corresponding to the target voice can be output intent label. In the embodiment of the present invention, whether it is a standard voice or a non-standard voice, the corresponding behavioral intention can be recognized through the target recognition model based on the acoustic features, which enhances the applicability of the voice recognition.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained based on these drawings without creative effort.

Fig. 1 is a flow chart of a speech recognition method shown according to an exemplary embodiment;

Fig. 2 is a flowchart of a speech recognition method according to another exemplary embodiment;

Fig. 3 is a schematic structural diagram of a speech recognition device according to an exemplary embodiment;

Fig. 4 is a schematic structural diagram of a speech recognition device according to another exemplary embodiment;

Fig. 5 is a schematic structural diagram of a speech recognition device according to another exemplary embodiment;

Fig. 6 is a schematic structural diagram of a terminal 600 according to another exemplary embodiment.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

Before describing the speech recognition method provided by the embodiment of the present invention in detail, the application scenario and implementation environment involved in the embodiment of the present invention will be briefly introduced.

First, the application scenarios involved in the embodiments of the present invention are briefly introduced.

In some application scenarios, in order to improve the convenience of operation, there is a need to control the terminal through voice. For example, the application scenarios include but are not limited to environments such as home and vehicle. In the process of controlling the terminal by voice, in order to know the behavioral intention corresponding to the voice, voice recognition is required. At present, in the process of speech recognition, it is necessary to convert the speech to be recognized into text, and then carry out semantic recognition on the converted text. However, in the above implementation process, text conversion and semantic recognition can only be performed on standard speech, and non-standard speech (such as dialect) cannot be recognized, resulting in poor applicability of speech recognition.

For this reason, the embodiment of the present invention provides a kind of speech recognition method, and this speech recognition method can recognize the corresponding behavioral intention through target recognition model based on the acoustic characteristic of speech, because this method does not need to recognize semantics, therefore, whether it is standard speech or All non-standard voices can realize voice recognition, which increases the applicability of voice recognition. For the specific implementation process, please refer to the embodiment shown in FIG. 1 or FIG. 2 below.

Next, briefly introduce the implementation environment involved in the embodiment of the present invention.

The speech recognition method provided by the embodiment of the present invention can be executed by the first terminal, and the first terminal can be configured with a speech collection device, for example, the speech collection device can be a microphone array, etc., for speech collection. In some embodiments, the first terminal can be any electronic product that can interact with the user in one or more ways such as keyboard, touch pad, touch screen, remote control, voice interaction or handwriting equipment, for example PC, mobile phone, smart phone, PDA, wearable device, handheld computer PPC, wearable device, tablet computer, smart car machine, smart TV, smart speaker, etc. In practical applications, when the first terminal is an electronic product capable of voice interaction with the user, it can be loaded/installed with a client terminal capable of recognizing, parsing, understanding, processing and responding to the user's natural language commands and outputting the response results. end (can be in the form of an APP), or the client can only perform speech recognition on the natural language commands input by the user but needs a corresponding server to analyze, understand, process and respond to the natural language commands of the user And return the response result to the client for output.

Further, the first terminal may be connected to at least one second terminal, and in a possible implementation manner, the at least one second terminal and the first terminal may both belong to the same user.

Here, the second terminal may be any electronic product capable of human-computer interaction with the user through one or more methods such as keyboard, touch pad, touch screen, remote control, voice interaction or handwriting equipment, such as PC, Mobile phones, smart phones, PDAs, wearable devices, PPCs, PPCs, wearable devices, tablets, smart cars, smart TVs, smart speakers, etc. In practical applications, when the second terminal is an electronic product capable of voice interaction with the user, it can be loaded/installed with a client terminal that can recognize, analyze, understand, process and respond to the user's natural language commands and output the response results. end (can be in the form of an APP), or the client can only perform speech recognition on the natural language commands input by the user but needs a corresponding server to analyze, understand, process and respond to the natural language commands of the user And return the response result to the client for output.

Here, both the first terminal and the second terminal include an electronic device that can automatically perform numerical calculation and information processing according to preset or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs) ), programmable gate array (FPGA), digital processor (DSP), embedded devices, etc.

After introducing the application scenarios and implementation environments involved in the embodiments of the present invention, the voice recognition method provided by the embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Please refer to FIG. 1. FIG. 1 is a flow chart of a voice recognition method according to an exemplary embodiment. The voice recognition method may be executed by the above-mentioned first terminal, and the voice recognition method may include the following implementation steps:

Step 101: Collect target speech to be recognized.

Step 102: Obtain the acoustic features of the target speech.

Step 103: call the target recognition model, input the acoustic features into the target recognition model, and output the behavioral intention label corresponding to the target voice, and the target recognition model is used to identify the behavioral intention corresponding to the voice according to the acoustic features of any voice .

Further, before invoking the target recognition model, the acoustic features of at least one voice training sample and the behavioral intent label corresponding to each voice training sample are acquired, based on the acoustic features of the at least one voice training sample and the behavior corresponding to each voice training sample The intent label is used to train the recognition model to be trained to obtain the target recognition model.

Wherein, the implementation process of obtaining the behavioral intention label corresponding to each speech training sample may include: obtaining at least one speech; determining the behavior operation corresponding to each speech in the at least one speech; generating the behavioral intention label corresponding to each behavior operation; The at least one speech is determined as the at least one speech training sample, and each generated behavioral intention label is determined as the behavioral intention label of the corresponding speech training sample.

Further, before acquiring at least one voice, according to the voiceprint feature of each voice, query whether the at least one voice comes from a target user, and the target user refers to a user who has an association relationship with the first terminal; when the at least one voice When the voices all come from the target user, the operation of acquiring at least one voice is performed.

In a possible implementation manner, according to the voiceprint feature of each voice, the implementation of querying whether the at least one voice comes from the target user may include: determining the difference between the voiceprint feature of each voice and the preset voiceprint feature When the difference value between the voiceprint feature of each voice and the preset voiceprint feature is less than a preset threshold, it is determined that the at least one voice is from the target user.

In some embodiments, based on the acoustic features of the at least one voice training sample and the behavioral intention label corresponding to each voice training sample, the recognition model to be trained is trained, and after the target recognition model is obtained, the second The terminals share the target recognition model, and the second terminal refers to a terminal that has an association relationship with the first terminal.

In the embodiment of the present invention, the target speech to be recognized is collected, and the acoustic features of the target speech are obtained. Call the target recognition model, because the target recognition model can recognize the behavioral intention corresponding to the voice according to the acoustic features of any voice, so after inputting the acquired acoustic features into the target recognition model, the behavior corresponding to the target voice can be output intent label. In the embodiment of the present invention, whether it is a standard voice or a non-standard voice, the corresponding behavioral intention can be recognized through the target recognition model based on the acoustic features, which enhances the applicability of the voice recognition.

Fig. 2 is a flow chart of a speech recognition method according to another exemplary embodiment. This embodiment uses the speech recognition method applied to the above-mentioned first terminal as an example for illustration. The speech recognition method may include the following implementations step:

Step 201: Collect target speech to be recognized.

When the user wants to control the first terminal by voice, he can speak directly to the voice collection device in the first terminal, such as a microphone array. Correspondingly, the first terminal can collect what the user said through the voice collection device, that is, collect the target voice to be recognized.

Further, the first terminal can collect the target voice to be recognized when receiving the voice recognition instruction, the voice recognition instruction can be triggered by the user, and the user can trigger it through a specified operation, and the specified operation can include a click operation, a slide operation etc. This is not limited in this embodiment of the present invention.

For example, the first terminal may provide a voice recognition option, and when the user wants to control the first terminal by voice, the user may click on the voice recognition option to trigger the voice recognition instruction. After receiving the speech recognition instruction, the first terminal collects the target speech to be recognized.

Step 202: Obtain the acoustic features of the target speech.

In order to facilitate the subsequent voice recognition of the target voice, the first terminal acquires the acoustic features of the target voice. Wherein, the acoustic feature may be used to describe at least one of loudness, pitch, frequency and timbre of the target speech.

Step 203: call the target recognition model, input the acoustic features into the target recognition model, and output the behavioral intention label corresponding to the target voice, and the target recognition model is used to identify the behavioral intention corresponding to the voice according to the acoustic features of any voice .

Since the target recognition model can be used to identify the behavioral intention corresponding to the voice according to the acoustic features of any voice, the first terminal can output the target voice corresponding to the target voice after inputting the acoustic features of the target voice into the target recognition model. behavioral intent label for .

In some embodiments, the behavioral intent tag can be a behavioral intent sequence. In other words, the behavioral intent tag can be used to describe a series of behavioral actions. For example, the behavioral intent tag can be used to describe "turn on the camera, Log in to WeChat, and then play music” series of actions, etc.

Further, after the first terminal determines the behavioral intention tag corresponding to the target voice, it may execute the behavioral operation corresponding to the behavioral intention tag. For example, if the behavioral intention tag is a camera start tag, the first terminal may start the installed camera application. In this way, the purpose of using the target voice to control the first terminal is achieved.

Further, before invoking the target recognition model, model training needs to be performed to obtain the above target recognition model. In a possible implementation, the training process may include: obtaining the acoustic features of at least one speech training sample and the corresponding Based on the acoustic features of the at least one voice training sample and the behavioral intention label corresponding to each voice training sample, the recognition model to be trained is trained to obtain the target recognition model.

That is, the first terminal obtains the acoustic features of at least one speech training sample and the behavioral intention label corresponding to each speech training sample, and combines the acquired acoustic features of the at least one speech training sample with the corresponding The behavioral intention label is input into the recognition model to be trained, and the recognition model is subjected to deep learning and training, so as to obtain a target recognition model that can recognize the behavioral intention of any speech based on the acoustic features of the speech.

In some embodiments, the recognition model to be trained may be a CNN (Convolutional Neural Networks, convolutional neural network) model, which is not limited in this embodiment of the present invention.

Wherein, the implementation process of obtaining the behavioral intention label corresponding to each speech training sample may include: obtaining at least one speech, and determining the behavior operation corresponding to each speech in the at least one speech. Generate a behavioral intention label corresponding to each behavioral operation, determine the at least one voice as the at least one voice training sample, and determine each generated behavioral intention label as the behavioral intention label of the corresponding voice training sample.

In a possible implementation manner, the behavior operation corresponding to each voice is executed when the operation instruction triggered for each voice is received. In some embodiments, the user may input at least one voice into the first terminal, and after inputting each voice in the at least one voice, may manually trigger a behavior operation for each voice. For example, the user can say "start Baidu map" to the first terminal in dialect, and then the user can manually start Baidu map. At this time, the first terminal may collect each voice in the at least one voice, and determine the corresponding behavior operation, for example, determine the behavior operation of starting Baidu map. Afterwards, the first terminal generates a behavioral intention label of the determined behavioral operation, for example, the behavioral intention label may be "start Baidu map label". The first terminal determines each acquired voice as each voice training sample in at least one voice training sample, and determines each generated behavior label as a behavior intention label of the corresponding voice training sample. In this way, the first terminal can train the recognition model to be trained based on the determined at least one speech training sample and the behavioral intention label corresponding to each speech training sample.

Of course, the above is only described by taking the behavior operation corresponding to each voice as an example to be executed when the operation instruction triggered by each voice is received. In another embodiment, the behavior operation corresponding to each voice may also be executed when the standard control voice entered for each voice is collected. Wherein, the standard control voice usually refers to Mandarin Chinese. In some embodiments, since the first terminal can recognize Mandarin Chinese, the user inputs at least one voice to the first terminal, and after inputting each voice in the at least one voice, the standard control voice can be used for each voice. A voice-triggered behavioral action. For example, the user can say "start Baidu map" to the first terminal in dialect, and then the user can control the first terminal to start Baidu map in Mandarin. At this time, the first terminal may collect each voice in the at least one voice, and determine the corresponding behavior operation, for example, determine the behavior operation of starting Baidu map. Afterwards, the first terminal generates a behavioral intention label of the determined behavioral operation, for example, the behavioral intention label may be "start Baidu map label". The first terminal determines each acquired voice as each voice training sample in at least one voice training sample, and determines each generated behavior label as a behavior intention label of the corresponding voice training sample.

Furthermore, before acquiring at least one voice, the source of the at least one voice can also be limited, and the corresponding processing includes: according to the voiceprint characteristics of each voice, inquiring whether the at least one voice comes from the target user, the target The user refers to a user associated with the first terminal, and when the at least one voice comes from the target user, the operation of acquiring at least one voice is performed.

In some embodiments, such as in a multi-person scenario, the first terminal may only perform training based on the voice of a target user associated with itself, for example, the target user may be the owner of the first terminal. In this case, before acquiring the at least one voice, the first terminal needs to determine whether the at least one voice comes from the target user. The first terminal may inquire whether the at least one voice is from the target user according to the voiceprint feature of each voice.

In a possible implementation manner, the above-mentioned implementation of querying whether the at least one voice is from the target user according to the voiceprint feature of each voice may include: determining the voiceprint feature of each voice and the preset voiceprint feature The difference value between; when the difference value between the voiceprint feature of each voice and the preset voiceprint feature is less than a preset threshold, it is determined that the at least one voice is from the target user.

The preset voiceprint feature may be preselected and stored in the first terminal, and the preset voiceprint feature may be the voiceprint feature of the target user. In this way, after the first terminal obtains the voiceprint feature of each voice, it can compare the acquired voiceprint feature of each voice with the pre-stored preset voiceprint feature, and determine that the voiceprint feature of each voice is consistent with the preset voiceprint feature. Set the difference value between voiceprint features.

In some embodiments, the acquired voiceprint features of each voice can be pattern-matched with pre-stored preset voiceprint features to determine the difference between the voiceprint features of each voice and the preset voiceprint features value. Wherein, the pattern matching method may include: a probability statistics method, an artificial neural network method, etc., which are not limited in this embodiment of the present application.

When the difference value is less than the preset threshold value, it means that the difference between the voiceprint feature of a certain voice being compared and the preset voiceprint feature is not large. At this time, it can be determined that the voice being compared comes from the target user. Conversely, if the difference value is greater than the preset threshold, it indicates that the voiceprint feature of a certain voice being compared differs greatly from the preset voiceprint feature. Therefore, it can be determined that the voice being compared does not come from the voiceprint feature. Target users. In this way, by comparing the voiceprint feature of each voice with a preset threshold, it can be determined whether the at least one voice comes from the target user.

Wherein, the preset threshold can be customized and set by the user according to actual needs, or can be set by default by the first terminal, which is not limited in this embodiment of the present invention.

Further, the above is only an example of querying whether the at least one voice comes from the target user according to the voiceprint characteristics of each voice. In another embodiment, it can be used to query whether the at least one voice comes from the target user based on other information The target user, for example, may also determine whether the at least one voice comes from the target user in combination with the sound source location of each voice.

In a possible implementation manner, the first terminal may determine whether any voice in the at least one voice comes from the target user according to the collected voiceprint features of any voice in the at least one voice. When it is determined that any voice is from the target user, then judge whether the sound source position of other voices in the at least one voice is the same as the sound source position of the any voice, that is, judge whether the at least one voice comes from same direction. When the sound source positions of the at least one voice are the same, it is determined that the at least one voice comes from the same user, that is, they all come from the target user. Otherwise, it may be determined that the at least one voice is not all from the same target user. Wherein, determining the sound source position of each speech may be determined based on parameters such as the received strength of the collected speech, which is not limited in this embodiment of the present invention.

It should be noted that, when the above at least one voice is not all from the target user, voices not from the target user in the at least one voice may be deleted, and then the deleted at least one voice is obtained.

So far, the speech recognition method involved in the embodiment of the present invention has been implemented. Further, since the user may use multiple terminals, in order to enable other terminals to perform speech recognition like the first terminal, after the first terminal trains the recognition model to be trained to obtain the target recognition model, it also The target recognition model can be shared, and for specific implementation, please refer to step 204 below.

Step 204: Share the target recognition model with a second terminal, where the second terminal refers to a terminal that has an association relationship with the first terminal.

In a possible implementation manner, the association relationship refers to the user belonging to the same user as the first terminal, or the association relationship refers to a connection relationship with the first terminal, or the association relationship refers to the The first terminal is under the same environment and so on.

Next, description will be made by taking the second terminal and the first terminal belonging to the same user as an example. For example, both the second terminal and the first terminal belong to the above-mentioned target user. That is to say, in order to enable the target user to control the second terminal by voice when using the second terminal, after the first terminal obtains the target recognition model through training, it can send the target recognition model to itself A second terminal belonging to the same user. Correspondingly, after receiving the object recognition model shared by the first terminal, the second terminal saves the object recognition model locally.

Of course, the first terminal can also receive the trained target recognition model sent by the second terminal, that is to say, the second terminal can also perform model training according to the above implementation method, and share the trained target recognition model with the first terminal. The first terminal receives the trained target recognition model shared by the second terminal. Further, when the target recognition model is stored in the first terminal, if the latest target recognition model shared by the second terminal is received, the The first terminal may delete the originally stored target recognition model, and save the latest target recognition model shared by the second terminal.

It is worth mentioning that the above-mentioned first terminal shares the target recognition model obtained after training with the second terminal, so that the second terminal saves the same target recognition model as the first terminal, so that the second terminal can directly use the target recognition model The recognition model performs speech recognition, avoiding the need for retraining itself, and reducing the number of times of training for the second terminal.

In the embodiment of the present invention, the target speech to be recognized is collected, and the acoustic features of the target speech are acquired. Call the target recognition model, because the target recognition model can recognize the behavioral intention corresponding to the voice according to the acoustic features of any voice, so after inputting the acquired acoustic features into the target recognition model, the behavior corresponding to the target voice can be output intent label. In the embodiment of the present invention, whether it is a standard voice or a non-standard voice, the corresponding behavioral intention can be recognized through the target recognition model based on the acoustic features, which enhances the applicability of the voice recognition.

Fig. 3 is a schematic structural diagram of a speech recognition device according to an exemplary embodiment. The speech recognition device may be implemented by software, hardware or a combination of both. The speech recognition device may include:

Acquisition module 301, is used for collecting target voice to be recognized;

The first acquiring module 302 is used to acquire the acoustic features of the target speech;

The calling module 303 is used to call the target recognition model, input the acoustic features into the target recognition model, and output the behavioral intention label corresponding to the target voice, and the target recognition model is used to Recognize the behavioral intention corresponding to the voice.

Optionally, please refer to Figure 4, the device also includes:

The second obtaining module 304 is used to obtain the acoustic features of at least one speech training sample and the behavioral intention label corresponding to each speech training sample;

The training module 305 is configured to train a recognition model to be trained based on the acoustic features of the at least one speech training sample and the behavioral intention label corresponding to each speech training sample, so as to obtain the target recognition model.

Optionally, the second acquiring module 304 is configured to:

Get at least one voice;

determining the behavior operation corresponding to each voice in the at least one voice;

Generate behavioral intent tags corresponding to each behavioral operation;

Determining the at least one speech as the at least one speech training sample, and determining each generated behavioral intention label as the behavioral intention label of the corresponding speech training sample.

Optionally, the second acquiring module 304 is also configured to:

According to the voiceprint feature of each voice, inquire whether the at least one voice is from a target user, where the target user refers to a user associated with the first terminal;

When the at least one voice is from the target user, the operation of acquiring at least one voice is performed.

Optionally, the second acquiring module 304 is also configured to:

Determine the difference between the voiceprint feature of each voice and the preset voiceprint feature;

When the difference values between the voiceprint feature of each voice and the preset voiceprint feature are smaller than a preset threshold, it is determined that the at least one voice is from the target user.

Optionally, referring to Figure 5, the device further includes:

A sharing module 306, configured to share the target recognition model with a second terminal, where the second terminal refers to a terminal that has an association relationship with the first terminal.

In the embodiment of the present invention, the target speech to be recognized is collected, and the acoustic features of the target speech are obtained. Call the target recognition model, because the target recognition model can recognize the behavioral intention corresponding to the voice according to the acoustic features of any voice, so after inputting the acquired acoustic features into the target recognition model, the behavior corresponding to the target voice can be output intent label. In the embodiment of the present invention, whether it is a standard voice or a non-standard voice, the corresponding behavioral intention can be recognized through the target recognition model based on the acoustic features, which enhances the applicability of the voice recognition.

It should be noted that: when the speech recognition device provided by the above-mentioned embodiments implements the speech recognition method, the division of the above-mentioned functional modules is used as an example for illustration. In practical applications, the above-mentioned function distribution can be completed by different functional modules according to needs. , which divides the internal structure of the device into different functional modules to complete all or part of the functions described above. In addition, the speech recognition device provided by the above embodiment and the speech recognition method embodiment belong to the same idea, and its specific implementation process is detailed in the method embodiment, and will not be repeated here.

Fig. 6 shows a structural block diagram of a terminal 600 provided by an exemplary embodiment of the present invention. The terminal 600 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, moving picture experts compress standard audio layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, moving picture experts compress standard audio layer 4) Player, laptop or desktop computer. The terminal 600 may also be called user equipment, portable terminal, laptop terminal, desktop terminal and other names.

Generally, the terminal 600 includes: a processor 601 and a memory 602 .

The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 601 can adopt at least one hardware form in DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array, programmable logic array) accomplish. The processor 601 may also include a main processor and a coprocessor, the main processor is a processor for processing data in a wake-up state, and is also called a CPU (Central Processing Unit, central processing unit); Low-power processor for processing data in standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is used for rendering and drawing content that needs to be displayed on the display screen. In some embodiments, the processor 601 may further include an AI (Artificial Intelligence, artificial intelligence) processor, where the AI processor is configured to process computing operations related to machine learning.

Memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory and non-volatile memory, such as one or more magnetic disk storage devices and flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 602 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 601 to realize the speech recognition provided by the method embodiment in this application method.

In some embodiments, the terminal 600 may optionally further include: a peripheral device interface 603 and at least one peripheral device. The processor 601, the memory 602, and the peripheral device interface 603 may be connected through buses or signal lines. Each peripheral device can be connected to the peripheral device interface 603 through a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 604 , a touch screen 605 , a camera 606 , an audio circuit 607 , a positioning component 608 and a power supply 609 .

The peripheral device interface 603 may be used to connect at least one peripheral device related to I/O (Input/Output, input/output) to the processor 601 and the memory 602 . In some embodiments, the processor 601, memory 602 and peripheral device interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one of the processor 601, memory 602 and peripheral device interface 603 or The two can be implemented on a separate chip or circuit board, which is not limited in this embodiment.

The radio frequency circuit 604 is configured to receive and transmit RF (Radio Frequency, radio frequency) signals, also called electromagnetic signals. The radio frequency circuit 604 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 604 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 604 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and the like. The radio frequency circuit 604 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes, but is not limited to: World Wide Web, Metropolitan Area Network, Intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), wireless local area network and/or WiFi (Wireless Fidelity, Wireless Fidelity) network. In some embodiments, the radio frequency circuit 604 may also include circuits related to NFC (Near Field Communication, short-range wireless communication), which is not limited in this application.

The display screen 605 is used to display a UI (User Interface, user interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 605 is a touch display screen, the display screen 605 also has the ability to collect touch signals on or above the surface of the display screen 605 . The touch signal can be input to the processor 601 as a control signal for processing. At this time, the display screen 605 can also be used to provide virtual buttons and/or virtual keyboards, also called soft buttons and/or soft keyboards. In some embodiments, there may be one display screen 605, which is set on the front panel of the terminal 600; in other embodiments, there may be at least two display screens 605, which are respectively arranged on different surfaces of the terminal 600 or in a folding design; In some other embodiments, the display screen 605 may be a flexible display screen, which is arranged on a curved surface or a folded surface of the terminal 600 . Even, the display screen 605 can also be set as a non-rectangular irregular figure, that is, a special-shaped screen. The display screen 605 may be made of LCD (Liquid Crystal Display, liquid crystal display), OLED (Organic Light-Emitting Diode, organic light-emitting diode) and other materials.

The camera assembly 606 is used to capture images or videos. Optionally, the camera component 606 includes a front camera and a rear camera. Usually, the front camera is set on the front panel of the terminal, and the rear camera is set on the back of the terminal. In some embodiments, there are at least two rear cameras, which are any one of the main camera, depth-of-field camera, wide-angle camera, and telephoto camera, so as to realize the fusion of the main camera and the depth-of-field camera to realize the background blur function. Combined with the wide-angle camera to realize panoramic shooting and VR (Virtual Reality, virtual reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 606 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. Dual color temperature flash refers to the combination of warm light flash and cold light flash, which can be used for light compensation under different color temperatures.

Audio circuitry 607 may include a microphone and speakers. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into electrical signals and input them to the processor 601 for processing, or input them to the radio frequency circuit 604 to realize voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal 600 . The microphone can also be an array microphone or an omnidirectional collection microphone. The speaker is used to convert the electrical signal from the processor 601 or the radio frequency circuit 604 into sound waves. The loudspeaker can be a conventional membrane loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, it is possible not only to convert electrical signals into sound waves audible to humans, but also to convert electrical signals into sound waves inaudible to humans for purposes such as distance measurement. In some embodiments, the audio circuit 607 may also include a headphone jack.

The positioning component 608 is used to locate the current geographic location of the terminal 600 to implement navigation or LBS (Location Based Service, location-based service). The positioning component 608 may be a positioning component based on the GPS (Global Positioning System, Global Positioning System) of the United States, the Beidou system of China, or the Galileo system of Russia.

The power supply 609 is used to supply power to various components in the terminal 600 . Power source 609 may be AC, DC, disposable or rechargeable batteries. When the power source 609 includes a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

In some embodiments, the terminal 600 further includes one or more sensors 610 . The one or more sensors 610 include, but are not limited to: an acceleration sensor 611 , a gyro sensor 612 , a pressure sensor 613 , a fingerprint sensor 614 , an optical sensor 615 and a proximity sensor 616 .

The acceleration sensor 611 can detect the acceleration on the three coordinate axes of the coordinate system established by the terminal 600 . For example, the acceleration sensor 611 can be used to detect the components of the acceleration of gravity on the three coordinate axes. The processor 601 may control the touch screen 605 to display a user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 611 . The acceleration sensor 611 can also be used for collecting game or user's motion data.

The gyro sensor 612 can detect the body direction and rotation angle of the terminal 600 , and the gyro sensor 612 can cooperate with the acceleration sensor 611 to collect 3D actions of the user on the terminal 600 . According to the data collected by the gyroscope sensor 612, the processor 601 can realize the following functions: motion sensing (such as changing the UI according to the user's tilt operation), image stabilization during shooting, game control and inertial navigation.

The pressure sensor 613 may be disposed on a side frame of the terminal 600 and/or a lower layer of the touch display screen 605 . When the pressure sensor 613 is installed on the side frame of the terminal 600 , it can detect the user's grip signal on the terminal 600 , and the processor 601 performs left and right hand recognition or shortcut operation according to the grip signal collected by the pressure sensor 613 . When the pressure sensor 613 is arranged on the lower layer of the touch screen 605, the processor 601 controls the operable controls on the UI interface according to the user's pressure operation on the touch screen 605. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

The fingerprint sensor 614 is used to collect the user's fingerprint, and the processor 601 recognizes the identity of the user according to the fingerprint collected by the fingerprint sensor 614, or the fingerprint sensor 614 recognizes the user's identity according to the collected fingerprint. When the identity of the user is identified as a trusted identity, the processor 601 authorizes the user to perform related sensitive operations, such sensitive operations include unlocking the screen, viewing encrypted information, downloading software, making payment, and changing settings. The fingerprint sensor 614 may be provided on the front, back or side of the terminal 600 . When the terminal 600 is provided with a physical button or a manufacturer's Logo, the fingerprint sensor 614 may be integrated with the physical button or the manufacturer's Logo.

The optical sensor 615 is used to collect ambient light intensity. In one embodiment, the processor 601 can control the display brightness of the touch screen 605 according to the ambient light intensity collected by the optical sensor 615 . Specifically, when the ambient light intensity is high, the display brightness of the touch screen 605 is increased; when the ambient light intensity is low, the display brightness of the touch screen 605 is decreased. In another embodiment, the processor 601 may also dynamically adjust shooting parameters of the camera assembly 606 according to the ambient light intensity collected by the optical sensor 615 .

The proximity sensor 616 , also called a distance sensor, is usually arranged on the front panel of the terminal 600 . The proximity sensor 616 is used to collect the distance between the user and the front of the terminal 600 . In one embodiment, when the proximity sensor 616 detects that the distance between the user and the front of the terminal 600 gradually decreases, the processor 601 controls the touch display screen 605 to switch from the bright screen state to the off screen state; when the proximity sensor 616 detects When the distance between the user and the front of the terminal 600 gradually increases, the processor 601 controls the touch display screen 605 to switch from the off-screen state to the on-screen state.

Those skilled in the art can understand that the structure shown in FIG. 6 does not constitute a limitation on the terminal 600, and may include more or less components than shown in the figure, or combine certain components, or adopt different component arrangements.

The embodiment of the present application also provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of the mobile terminal, the mobile terminal can execute the above-mentioned embodiment shown in FIG. 1 or FIG. 2 Provided speech recognition methods.

The embodiment of the present application also provides a computer program product containing instructions, which, when run on a computer, causes the computer to execute the voice recognition method provided in the above embodiment shown in FIG. 1 or FIG. 2 .

Those of ordinary skill in the art can understand that all or part of the steps for implementing the above embodiments can be completed by hardware, and can also be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium. The above-mentioned The storage medium mentioned may be a read-only memory, a magnetic disk or an optical disk, and the like.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.

Claims (14)

1. a kind of audio recognition method is applied in first terminal, which is characterized in that the method includes：

Acquire target voice to be identified；

Obtain the acoustic feature of the target voice；

The acoustic feature is input in the Model of Target Recognition by invocation target identification model, exports the target voice Corresponding behavior intention labels, the Model of Target Recognition are used to identify that the voice corresponds to according to the acoustic feature of any voice Behavior be intended to.

2. the method as described in claim 1, which is characterized in that before the invocation target identification model, further include：

Obtain the acoustic feature and the corresponding behavior intention labels of each voice training sample of at least one voice training sample；

The corresponding behavior meaning of acoustic feature and each voice training sample based at least one voice training sample Icon label are treated trained identification model and are trained, obtain the Model of Target Recognition.

3. method as claimed in claim 2, which is characterized in that obtain the corresponding behavior of each voice training sample and be intended to mark Label, including：

Obtain at least one voice；

Determine the corresponding behavior operation of each voice at least one voice；

It generates each behavior and operates corresponding behavior intention labels；

At least one voice is determined as at least one voice training sample, and each behavior of generation is intended to Label is determined as the behavior intention labels of corresponding voice training sample.

4. method as claimed in claim 3, which is characterized in that before at least one voice of acquisition, further include：

According to the vocal print feature of each voice, inquire whether at least one voice is all from target user；

When at least one voice is all from the target user, the operation for obtaining at least one voice is executed.

5. method as claimed in claim 4, which is characterized in that the vocal print feature according to each voice inquires institute State whether at least one voice is all from target user, including：

Determine the difference value between the vocal print feature and default vocal print feature of each voice；

When the difference value between the vocal print feature of each voice and the default vocal print feature is respectively less than predetermined threshold value, really Fixed at least one voice is all from the target user.

6. method as claimed in claim 2, which is characterized in that the acoustics based at least one voice training sample Feature and the corresponding behavior intention labels of each voice training sample, treat trained identification model and are trained, obtain After the Model of Target Recognition, further include：

Share the Model of Target Recognition to second terminal, the second terminal refers to having incidence relation with the first terminal Terminal.

7. a kind of speech recognition equipment, it is applied in first terminal, which is characterized in that described device includes：

Acquisition module, for acquiring target voice to be identified；

First acquisition module, the acoustic feature for obtaining the target voice；

Calling module is used for invocation target identification model, the acoustic feature is input in the Model of Target Recognition, exports The corresponding behavior intention labels of the target voice, the Model of Target Recognition are used to be identified according to the acoustic feature of any voice The corresponding behavior of the voice is intended to.

8. device as claimed in claim 7, which is characterized in that described device further includes：

Second acquisition module, acoustic feature and each voice training sample for obtaining at least one voice training sample correspond to Behavior intention labels；

Training module is used for the acoustic feature based at least one voice training sample and each voice training sample Corresponding behavior intention labels are treated trained identification model and are trained, obtain the Model of Target Recognition.

9. device as claimed in claim 8, which is characterized in that second acquisition module is used for：

Obtain at least one voice；

Determine the corresponding behavior operation of each voice at least one voice；

It generates each behavior and operates corresponding behavior intention labels；

At least one voice is determined as at least one voice training sample, and each behavior of generation is intended to Label is determined as the behavior intention labels of corresponding voice training sample.

10. device as claimed in claim 9, which is characterized in that second acquisition module is additionally operable to：

According to the vocal print feature of each voice, inquire whether at least one voice is all from target user, the mesh Mark user refers to the user for having incidence relation with the first terminal；

When at least one voice is all from the target user, the operation for obtaining at least one voice is executed.

11. device as claimed in claim 10, which is characterized in that second acquisition module is additionally operable to：

Determine the difference value between the vocal print feature and default vocal print feature of each voice；

When the difference value between the vocal print feature of each voice and the default vocal print feature is respectively less than predetermined threshold value, really Fixed at least one voice is all from the target user.

12. device as claimed in claim 8, which is characterized in that described device further includes：

Sharing module, for sharing the Model of Target Recognition to second terminal, the second terminal refers to and first end Hold the terminal with incidence relation.

13. a kind of computer readable storage medium, instruction is stored on the computer readable storage medium, which is characterized in that The method described in any one of claim 1-6 is realized when described instruction is executed by processor.

14. a kind of computing device, including：

One or more processors；

Memory, for storing one or more programs,

When one or more of programs are executed by one or more of processors so that one or more of processors Execute the method as described in any one of claim 1-6.