Speech recognition method, apparatus, device, system and program product

Abstract

The embodiment of the present specification provides a voice recognition method, which determines a target user and a voice scene corresponding to first voice information by performing voice recognition on the first voice information; then configures a black and white list corresponding to the target user based on the voice scene, the black and white list being used for adjusting a voice end value; and in response to triggering of second voice information, updates a list resource according to the black and white list; and then determines a voice end value corresponding to the second voice information through the updated list resource, so as to recognize the second voice information based on the voice end value. Thus, a personalized voice end value configuration process is realized. Since the black and white list configuration is combined with the voice features of the user, and the list resource is maintained through the black and white list, the voice end value can be adapted to different users, the completeness of the content after the user voice input is improved, and the accuracy of voice recognition is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to speech recognition methods, devices, equipment, systems, and program products. Background Art

[0002] With the increasing number of smart devices, controlling devices through voice has become a common method. For example, voice activity detection technology can be used to identify voice activity segments and non-voice activity segments in audio signals for voice recognition.

[0003] Typically, voice activity detection (EOS) technology segments speech by determining whether the silence at the end of a valid speech segment reaches the end-of-speech value. However, in phrase stream interaction, such as in in-vehicle voice recognition, the EOS duration is often fixed, leading to premature truncation and long wait times. This can easily cause users to miss content during voice input, impacting voice recognition accuracy. Summary of the Invention

[0004] In view of this, the present application provides a speech recognition method to improve the accuracy of speech recognition.

[0005] According to a first aspect of an embodiment of the present application, a method for speech recognition is provided, which can be applied to a system or program including a speech recognition function in a terminal device, and specifically includes:

[0006] Performing voice recognition on the first voice information to determine a target user and a voice scenario corresponding to the first voice information;

[0007] Configuring a blacklist and whitelist corresponding to the target user based on the voice scenario, wherein the blacklist and whitelist is used to adjust the voice end value;

[0008] In response to a trigger of a second voice message, updating the list resource according to the blacklist and whitelist, wherein the second voice message is a voice message in a subsequent conversation of the first voice message;

[0009] The voice end value corresponding to the second voice information is determined through the updated list resource, so as to recognize the second voice information based on the voice end value.

[0010] Optionally, in some possible implementations, performing speech recognition on the first voice information to determine a target user and a speech scenario corresponding to the first voice information includes:

[0011] Get voiceprint registration information;

[0012] Comparing the first voice information with the voiceprint registration information to determine the target user;

[0013] Performing voice recognition on the first voice information to determine a voice recognition result associated with the target user;

[0014] Perform semantic discrimination on the speech recognition result to determine the speech scene corresponding to the first speech information.

[0015] Optionally, in some possible implementations, the speech scene is a truncated scene, and performing semantic discrimination on the speech recognition result to determine the speech scene corresponding to the first speech information includes:

[0016] Acquire first content and second content that are adjacent to each other in the speech recognition result;

[0017] Determining integrity information corresponding to the first content and the second content, wherein the integrity information is set based on content integrity and semantic integrity;

[0018] If the completeness information indicates that the first content and the second content are complete, and the semantically associated content of the first content and the second content is complete, it is determined that the voice scene corresponding to the first voice information is the truncated scene.

[0019] Optionally, in some possible implementations, the voice scene is a long-wait scene, and performing semantic discrimination on the voice recognition result to determine the voice scene corresponding to the first voice information includes:

[0020] Determining execution information in the speech recognition result for triggering execution of an action;

[0021] If the semantics indicated by the execution information are complete and the action corresponding to the execution information has not been executed, it is determined that the voice scene corresponding to the first voice information is the long waiting scene.

[0022] Optionally, in some possible implementations, if the semantics indicated by the execution information are complete and the execution information is used to indicate a target action, determining that the voice scene corresponding to the first voice information is the long-wait scene includes:

[0023] If the semantics indicated by the execution information are complete and the execution information is used to indicate the target action, then generating prompt content indicating the response time;

[0024] In response to a user confirmation operation on the prompt content, it is determined that the voice scene corresponding to the first voice information is the long-wait scene.

[0025] Optionally, in some possible implementations, configuring a blacklist and whitelist corresponding to the target user based on the voice scenario includes:

[0026] If the voice scene is a truncated scene, obtaining semantically incomplete voice recognition content in the truncated scene, and configuring the semantically incomplete voice recognition content to a blacklist in the blacklist and whitelist corresponding to the target user;

[0027] If the voice scene is a long-wait scene, semantically complete voice recognition content in the long-wait scene is obtained, and the semantically complete voice recognition content is configured into a whitelist in the blacklist and whitelist corresponding to the target user.

[0028] Optionally, in some possible implementations, in response to the triggering of the second voice information, updating the list resource according to the blacklist and whitelist includes:

[0029] In response to the triggering of the second voice message, the blacklist and whitelist are sent to the cloud, the second voice message is subjected to voiceprint recognition in the cloud to determine the target user, and the list resource corresponding to the target user is loaded;

[0030] The list resource corresponding to the target user is updated according to the blacklist and whitelist.

[0031] Optionally, in some possible implementations, determining the voice end value corresponding to the second voice information using the updated list resource, and recognizing the second voice information based on the voice end value, includes:

[0032] Matching the voice content corresponding to the second voice information with the updated list resource to obtain list hit information;

[0033] determining a hit scenario indicated by the list hit information;

[0034] The voice end value corresponding to the second voice information is determined based on the hit scenario, so as to recognize the second voice information based on the voice end value.

[0035] Optionally, in some possible implementations, the method further includes:

[0036] If the list hit information indicates that there is no hit scenario matching the second voice information in the list resource, determining a user group associated with a target user corresponding to the second voice information;

[0037] Obtaining a semantic recognition model corresponding to the user group;

[0038] The voice end value corresponding to the second voice information is determined based on the list resource corresponding to the user group, so as to recognize the second voice information based on the voice end value.

[0039] Optionally, in some possible implementation manners, the training process of the semantic recognition model corresponding to the user group comprises:

[0040] Obtaining training corpus;

[0041] Classifying the training corpus according to a preset dimension based on a user group type, to obtain training data corresponding to each user group, wherein the preset dimension is set based on a language type dimension and an object description dimension;

[0042] Training a semantic recognition model corresponding to each user group according to the training data.

[0043] According to a second aspect of the embodiment of the present application, a voice recognition device is provided, comprising:

[0044] A recognition unit, configured to perform voice recognition on first voice information, to determine a target user and a voice scene corresponding to the first voice information;

[0045] A configuration unit, configured to configure a black and white list corresponding to the target user based on the voice scene, wherein the black and white list is used to adjust a voice end value;

[0046] The configuration unit is further configured to update the list resource according to the black and white list in response to triggering of second voice information, wherein the second voice information is voice information in a subsequent session of the first voice information;

[0047] The recognition unit is further configured to determine a voice end value corresponding to the second voice information through the updated list resource, to perform recognition on the second voice information based on the voice end value.

[0048] Optionally, in some possible implementation manners, the recognition unit is configured to, when performing voice recognition on first voice information to determine a target user and a voice scene corresponding to the first voice information, obtain voiceprint registration information; compare the first voice information with the voiceprint registration information to determine the target user; perform voice recognition on the first voice information to determine a voice recognition result associated with the target user; and perform semantic discrimination on the voice recognition result to determine the voice scene corresponding to the first voice information.

[0049] Optionally, in some possible implementations, the recognition unit is used to obtain the first content and the second content whose contents are adjacent in the speech recognition result when the speech scene is a truncated scene and the speech recognition result is semantically judged to determine the speech scene corresponding to the first speech information; determine the completeness information corresponding to the first content and the second content, the completeness information being set based on content integrity and semantic integrity; if the completeness information indicates that the contents of the first content and the second content are complete, and the contents of the first content and the second content after semantic association are complete, then determine that the speech scene corresponding to the first speech information is the truncated scene.

[0050] Optionally, in some possible implementations, the recognition unit is used to determine the execution information in the voice recognition result for triggering the execution of an action when the voice scene is a long-waiting scene and semantic discrimination is performed on the voice recognition result to determine the voice scene corresponding to the first voice information; if the semantics indicated by the execution information are complete and the action corresponding to the execution information is not executed, it is determined that the voice scene corresponding to the first voice information is the long-waiting scene.

[0051] Optionally, in some possible implementations, the recognition unit is used to generate prompt content indicating the response time when the semantics indicated by the execution information are complete and the execution information is used to indicate the target action, and to determine that the voice scene corresponding to the first voice information is the long waiting scene; in response to the user's confirmation operation on the prompt content, the voice scene corresponding to the first voice information is the long waiting scene.

[0052] Optionally, in some possible implementations, the configuration unit is used to, when configuring the blacklist and whitelist corresponding to the target user based on the voice scene, obtain semantically incomplete voice recognition content in the truncated scene if the voice scene is a truncated scene, and configure the semantically incomplete voice recognition content to the blacklist in the blacklist and whitelist corresponding to the target user; if the voice scene is a long-wait scene, obtain semantically complete voice recognition content in the long-wait scene, and configure the semantically complete voice recognition content to the whitelist in the blacklist and whitelist corresponding to the target user.

[0053] Optionally, in some possible implementation manners, the configuration unit is configured to, in response to the trigger of the second voice information, send the black and white list to the cloud end for voiceprint recognition of the second voice information to determine a target user and load the list resource corresponding to the target user according to the black and white list, and update the list resource corresponding to the target user according to the black and white list.

[0054] Optionally, in some possible implementation manners, the identification unit is configured to, in the process of determining the voice end value corresponding to the second voice information based on the updated list resource, match the voice content corresponding to the second voice information with the updated list resource to obtain list hit information, determine a hit scene indicated by the list hit information, and determine the voice end value corresponding to the second voice information based on the hit scene, and identify the second voice information based on the voice end value.

[0055] Optionally, in some possible implementation manners, the identification unit is further configured to, if the list hit information indicates that there is no hit scene in the list resource that matches the second voice information, determine a user group to which a target user corresponding to the second voice information belongs, obtain a semantic recognition model corresponding to the user group, and determine the voice end value corresponding to the second voice information based on the list resource corresponding to the user group, and identify the second voice information based on the voice end value.

[0056] Optionally, in some possible implementation manners, the identification unit is further configured to obtain training corpus, classify the training corpus according to a preset dimension based on a user group type to obtain training data corresponding to each user group, and train a semantic recognition model corresponding to each user group according to the training data, wherein the preset dimension is set based on a language type dimension and an object description dimension.

[0057] According to a third aspect of the embodiments of the present application, a voice recognition device is provided, comprising an input-output component and a processor.

[0058] The input-output component is configured to obtain voice information.

[0060] The processor is configured to perform voice content recognition on the voice information obtained by the input-output component by executing the voice recognition method in the first aspect or any implementation manner of the first aspect.

[0061] According to a fourth aspect of the embodiments of the present application, a voice recognition system is provided, comprising an interactive client and a server.

[0061] The interactive client is used to obtain voice information and send the voice information to the server, and to display the voice recognition results output by the server;

[0062] The server is configured to perform voice content recognition on the voice information obtained by the interactive client by executing the voice recognition method described in the first aspect or any implementation of the first aspect.

[0063] According to a fifth aspect of an embodiment of the present application, a computer program product is provided, comprising: a computer program, which, when executed by a processor, implements the speech recognition method described in the first aspect or any implementation manner of the first aspect.

[0064] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0065] By performing voice recognition on the first voice message, the target user and voice scenario corresponding to the first voice message are determined; then, based on the voice scenario, a blacklist and whitelist corresponding to the target user are configured, and the blacklist and whitelist are used to adjust the voice end value; and in response to the triggering of the second voice message, the list resource is updated according to the blacklist and whitelist, and the second voice message is a voice message in the subsequent conversation of the first voice message; and then, the voice end value corresponding to the second voice message is determined through the updated list resource, so that the second voice message is recognized based on the voice end value. This realizes a personalized voice end value configuration process. Since the blacklist and whitelist configuration is combined with the user's voice characteristics, and the list resource is maintained through the blacklist and whitelist, the voice end value can be adapted to different users, improving the integrity of the content after the user's voice input and improving the accuracy of voice recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present application or the technical solutions in 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 merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0067] Figure 1 A diagram of the network architecture that runs the speech recognition system;

[0068] Figure 2 A flow chart of speech recognition provided in an embodiment of the present application;

[0069] Figure 3 A flowchart of a speech recognition method provided in an embodiment of the present application;

[0070] Figure 4 A schematic diagram of a blacklist and whitelist configuration process provided in an embodiment of the present application;

[0071] Figure 5 A schematic diagram of another process for taking effect of a list resource provided in an embodiment of the present application;

[0072] Figure 6 A schematic diagram of another configuration of a speech recognition model provided in an embodiment of the present application;

[0073] Figure 7 A schematic diagram of the structure of a speech recognition device provided in an embodiment of the present application;

[0074] Figure 8 A schematic diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0076] It should be understood that the speech recognition method provided in this application can be applied to a system or program containing a speech recognition function in a terminal device, such as an intelligent control application. Specifically, the speech recognition system can be run on Figure 1 In the network architecture shown in Figure 1 As shown in the figure, it is a network architecture diagram of the operation of the speech recognition system. As can be seen from the figure, the speech recognition system can provide a speech recognition process with multiple information sources, that is, the voice information is determined by the acquisition operation on the terminal side, and then sent to the server to determine the corresponding voice end value in the voice information, and then perform speech recognition; It can be understood that, Figure 1 A variety of terminal devices are shown in FIG. , which may be computer devices. In actual scenarios, more or fewer types of terminal devices may participate in the speech recognition process. The specific number and type depend on the actual scenario and are not limited here. In addition, Figure 1 One server is shown in the figure, but in actual scenarios, multiple servers may be involved, especially in multi-disciplinary scenarios. The specific number of servers depends on the actual scenario.

[0077] In this embodiment, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be connected directly or indirectly via wired or wireless communication, and the terminal and server can be connected to form a blockchain network, which is not limited in this application.

[0078] It can be understood that the above-mentioned voice recognition system can run on a personal mobile terminal, for example, as an application such as a smart home, or it can run on a server, or it can run on a third-party device to provide voice recognition to obtain the voice recognition processing results of the information source; the specific voice recognition system can be run in the above-mentioned device in the form of a program, or it can be run as a system component in the above-mentioned device, or it can be used as a cloud service program. The specific operation mode depends on the actual scenario and is not limited here.

[0079] With the increasing number of smart devices, controlling devices through voice has become a common method. For example, voice activity detection technology can be used to identify voice activity segments and non-voice activity segments in audio signals for voice recognition.

[0080] Typically, VAD segments speech by determining whether the silence at the end of a valid speech segment reaches the End of Speech (EOS) duration. However, in phrase stream interaction, such as in-vehicle voice recognition, the EOS duration is often fixed, leading to premature truncation and long waiting times. This can easily cause users to miss content during voice input, impacting speech recognition accuracy.

[0081] In order to solve the above problems, this application proposes a speech recognition method, which is applied to Figure 2 In the speech recognition process framework shown in Figure 2 As shown, this is a flow architecture diagram of speech recognition provided by an embodiment of the present application, which obtains speech information through interactive operations of the terminal, and determines the speech end value based on the black and white list resources of the server, and then performs the speech recognition process.

[0082] It is understandable that the speech recognition method provided in this application can be written as a program to serve as a processing logic in a hardware system, or as a speech recognition device that implements the above processing logic in an integrated or external manner. As an implementation method, the speech recognition device performs speech recognition on the first voice information to determine the target user and voice scene corresponding to the first voice information; then configures the black and white lists corresponding to the target user based on the voice scene, and the black and white lists are used to adjust the voice end value; and in response to the triggering of the second voice information, updates the list resources according to the black and white lists, and the second voice information is the voice information in the subsequent conversation of the first voice information; and then determines the voice end value corresponding to the second voice information through the updated list resources, so as to recognize the second voice information based on the voice end value. In this way, a personalized voice end value configuration process is realized. Since the black and white lists are configured in combination with the user's voice characteristics, and the list resources are maintained through the black and white lists, the voice end value can be adapted to different users, thereby improving the integrity of the content after the user's voice input and improving the accuracy of speech recognition.

[0083] In combination with the above process architecture, the speech recognition method in this application will be introduced below. Figure 3 , Figure 3 This is a flow chart of a speech recognition method provided in an embodiment of the present application, which includes at least the following steps:

[0084] 301. Perform voice recognition on first voice information to determine a target user and a voice scenario corresponding to the first voice information.

[0085] In this embodiment, speech recognition is performed based on voice activity detection technology (VAD), which is used to identify speech activity segments and non-speech activity segments in audio signals. Voice activity refers to audio segments that contain voice information, while non-speech activity refers to audio segments that do not contain voice information. Specifically, this embodiment uses semantic VAD to judge the semantic completeness of voice information, obtains the semantic completeness probability of the recognized content, and then dynamically adjusts the tail silence waiting time during the speech recognition process, wherein the tail silence waiting time is the end of speech value (End of Speech), which is used to segment the speech segments for recognition.

[0086] In one possible scenario, the speech recognition process of this embodiment is applied to smart device control scenarios such as in-vehicle voice control and smart home voice control. The voice information in this scenario is characterized by concise language and multi-phrase control. Compared with general long text voice content, it is more prone to voice truncation and long waiting problems. Therefore, targeted voice end values ​​are designed to address these problems to avoid the impact of the above scenarios on voice integrity.

[0087] Specifically, the dynamic configuration process of the above-mentioned voice end value is the process of configuring the blacklist and whitelist based on the voice content of different users in the voice interruption and long waiting scenarios. Figure 4 As shown, Figure 4 A flow chart of a black and white list configuration is provided for an embodiment of the present application; the figure shows the process of configuring black and white lists for users in a targeted manner according to different scenarios, that is, first obtaining voiceprint registration information. The voiceprint registration process takes into account that users may have different accents and belong to different age groups. Their speaking speed and speaking habits are different. For example, the voice habits of users in Shandong Province often have inverted sentences, so personalized registration configuration is performed for each user; then the first voice information is compared with the voiceprint registration information to determine the target user; and voice recognition is performed on the first voice information to determine the voice recognition result associated with the target user; and then semantic judgment is performed on the voice recognition result to determine the voice scene corresponding to the first voice information.

[0088] Voice scenarios include premature interruption, long wait, and other scenarios. In premature interruption, the user pauses in their speech, terminating the interaction before they finish speaking, resulting in incomplete semantics. In long wait, the speech recognition system waits for a fixed EOS duration even after the user has finished speaking, resulting in longer recognition response times.

[0089] Therefore, for the process of determining the truncated scene, first obtain the first content and the second content whose content is adjacent in the speech recognition result; then determine the completeness information corresponding to the first content and the second content, and the completeness information is set based on the content integrity and semantic integrity; if the completeness information indicates that the content of the first content and the second content is complete, and the content after the semantic association of the first content and the second content is complete, then determine that the speech scene corresponding to the first speech information is a truncated scene.

[0090] Specifically, the semantic discrimination process of the truncated scene is to compare the semantic completeness of the user's previous and subsequent voice recognition contents (first content and second content) within a limited time to determine the truncated scene: if the user performs voice recognition twice consecutively within a limited time, the semantics of the first recognition are incomplete, the semantics of the second recognition are relatively complete, and the result of the second recognition is semantically complete after some words are added to the end of the first recognition result, then the scene is determined to be a truncated scene.

[0091] In addition, for the semantic discrimination process of the long waiting scene, the execution information used to trigger the execution of the action in the voice recognition result is first determined; if the semantics indicated by the execution information is complete and the action corresponding to the execution information is not executed, then the voice scene corresponding to the first voice information is determined to be a long waiting scene.

[0092] Specifically, the semantic discrimination process of the long waiting scene is that the user only performs voice recognition once within a limited time, and it is judged that the recognition content is semantically complete and triggers a certain type of execution action (execution information), then the scene is considered to be a suspected long waiting scene.

[0093] Furthermore, different users may have different perceptions of what constitutes a long wait, and therefore user confirmation is required. That is, if the semantics of the execution information indicated are complete and the execution information is used to indicate the target action, a prompt content indicating the response time is generated; then, in response to the user's confirmation operation on the prompt content, it is determined that the voice scene corresponding to the first voice information is a long wait scene.

[0094] In one possible scenario, for the configuration of the prompt content, that is, when the user needs to confirm, the system pops up a window to ask the user whether the voice recognition response time they feel is too long and whether it can be optimized. After the user gives an affirmative reply (confirmation operation), the confirmation operation can be a voice reply or a click reply, and the scene can be determined as a long waiting scene.

[0095] In one possible scenario, the voice recognition process involves interaction between the terminal and the cloud, and multiple users are using the terminal. In this scenario, user management is performed by registering voiceprints for users using the voice recognition service. This means that more than one user is using a smart car or smart home device. In this embodiment, users using the voice recognition service have already registered their voiceprint information, and a blacklist and whitelist are established on the terminal for each user. The user list resource for newly registered voiceprints is empty. Furthermore, when a user performs voice recognition, the cloud-based voice recognition system uses a semantic discrimination module to capture interruption scenarios and long wait scenarios for each user.

[0096] Through the above blacklist and whitelist configuration, the personalized blacklist and whitelist based on semantic VAD recognition are updated and stored in the end-side device. The end-side device will upload it to the cloud-based speech recognition service the next time speech recognition is performed.

[0097] 302. Configure a blacklist and whitelist corresponding to the target user based on the voice scenario. The blacklist and whitelist are used to adjust the voice end value.

[0098] In this embodiment, the blacklist in the black and white list is used to adjust the voice end value to the upper limit, with the purpose of collecting complete voice content as much as possible; and the whitelist in the black and white list is used to adjust the voice end value to the lower limit, with the purpose of saving time as much as possible while collecting complete voice content.

[0099] Therefore, for the configuration process of the black and white lists, if the voice scene is a truncated scene, the semantically incomplete voice recognition content in the truncated scene is obtained, and the semantically incomplete voice recognition content is configured to the blacklist in the black and white list corresponding to the target user. That is, for the truncated scene, the system captures the semantically incomplete voice recognition content for the first time and adds it to the user's blacklist.

[0100] For long-wait scenarios, the system obtains semantically complete speech recognition content in the long-wait scenario and configures the semantically complete speech recognition content to the whitelist in the blacklist and whitelist corresponding to the target user. That is, for long-wait scenarios, the system adds semantically complete speech recognition content to the user's whitelist.

[0101] 303. In response to the triggering of the second voice information, the list resource is updated according to the blacklist and whitelist, where the second voice information is the voice information in the subsequent conversation of the first voice information.

[0102] In this embodiment, the list resource is a collection of black and white lists, which can be configured locally, but considering the huge amount of data, it can also be configured in the cloud to improve the efficiency of speech recognition; this embodiment takes the cloud configuration as an example for explanation.

[0103] Specifically, the second voice information is the voice information in the subsequent conversation, which can be the next round of conversation or the specified subsequent conversation (for example, the conversation specified by the user to start voice recognition optimization); therefore, in the process of updating the list resources in the cloud, the black and white lists can be sent to the cloud in response to the trigger of the second voice information, and the second voice information can be recognized by voiceprint in the cloud to determine the target user, and the list resources corresponding to the target user are loaded; then the list resources corresponding to the target user are updated according to the black and white lists.

[0104] The process of updating the list resources is a hot update process. That is, after the next round of conversation begins, the personalized black, white and gray list resources of all users of the end-side device are uploaded to the cloud-based voice recognition service. The recognition service loads the black and white list resources of the corresponding user based on the voiceprint information, completes the hot update and takes effect in the current conversation.

[0105] It can be seen that the semantic VAD black, white and gray list hot update mechanism can realize the online upgrade of the semantic VAD service, avoid the restart of the speech recognition service, and greatly reduce the development and operation and maintenance costs of the speech recognition service while speeding up the update speed.

[0106] The cloud-based voice recognition module combines voiceprint identification with user feedback from the voice recognition system and the corresponding voice recognition results. The semantic identification module extracts personalized blacklist and whitelist resources based on the user's feedback and voice recognition results, and stores them on the client. The client-side user-level blacklist and whitelist resources are uploaded to the cloud at the start of voice recognition and take effect during the current recognition session, enabling hot updates of semantic VAD personalized list resources.

[0107] 304. Determine the voice end value corresponding to the second voice information through the updated list resource, so as to recognize the second voice information based on the voice end value.

[0108] In this embodiment, the process of determining the voice end value corresponding to the second voice information, that is, determining the hit status of the second voice information for the scene, is firstly based on the voice content corresponding to the second voice information and the updated list resource to obtain the list hit information; then the hit scene indicated by the list hit information is determined; and based on the hit scene, the voice end value corresponding to the second voice information is determined, so as to identify the second voice information based on the voice end value, that is, the second voice information is segmented according to the time length corresponding to the voice end value, and then the segmented content is voice recognized.

[0109] It is understandable that, considering that users may have different accents and belong to different age groups, their speaking speeds and speaking habits are different. For example, the speech habits of users in Shandong often have inverted sentences. That is, in a scenario with a large number of users, the amount of data corresponding to maintaining the list resources is huge, and configuring the list resources one by one consumes too much computing power. At this time, the EOS value can be determined based on the user group. That is, for the list hit information indicating that there is no hit scenario matching the second voice information in the list resource, the user group associated with the target user corresponding to the second voice information is determined; then the semantic recognition model corresponding to the user group is obtained; and the voice end value corresponding to the second voice information is determined based on the list resource corresponding to the user group, so as to recognize the second voice information based on the voice end value.

[0110] Therefore, combining the above-mentioned list resources with the speech recognition process of the user group, we can use Figure 5 The process shown, Figure 5A flow chart of another list resource effectiveness provided in an embodiment of the present application; the figure shows that when the next round of voice recognition session (second voice information) is started, the end side uploads the blacklist and whitelist of all users who have registered voiceprints on the end side to the cloud voice recognition service. The cloud recognition service dynamically uses the list resource of the corresponding user according to the voiceprint of this voice recognition, and adds it to the public blacklist and whitelist resource of the recognition service in the form of hot update, and takes effect in the current voice recognition session. At the same time, the voice recognition service performs audio attribute analysis. For unregistered users (or in the case of content in the hit list resource), the user group to which the user belongs is determined based on the user's accent, age, and gender. The model resource used by the semantic VAD service is the model resource of this user group.

[0111] Furthermore, for the semantic recognition model configured for different user groups, the training process is as follows: Figure 6 As shown, Figure 6 A configuration diagram of another speech recognition model provided for an embodiment of the present application; the figure shows that training corpus is first obtained; then the training corpus is classified according to preset dimensions based on user group type to obtain training data corresponding to each user group, and the preset dimensions are based on language type dimensions (such as dialect type, language system, etc.) and object description dimension settings (such as age, gender, etc.); and then the semantic recognition model corresponding to each user group is trained according to the training data.

[0112] In one possible scenario, the cloud-based recognition service divides users into different user groups (e.g., adult females speaking Shandong dialect, male children speaking Minnan dialect, etc.) based on desensitized information such as their accent, age, and gender. The cloud-based recognition service regularly aggregates blacklist and whitelist resources for different user groups, cleans them, and uses them as fine-tuned training data for the personalized semantic VAD model for each user group. This training generates semantic VAD models for different user groups.

[0113] It is understandable that the blacklist and whitelist of the first voice message (current session) will also update the semantic recognition model of each of the above-mentioned user groups. That is, the list resources in step 303 can be configured by different user groups. In other words, the semantic recognition model is updated and launched online with the cloud recognition service. In addition to the personalized blacklist and whitelist resources, users can also use the semantic VAD model resources of their user group, thus enhancing the generalization capability of the user-level personalized semantic VAD service. For new users who do not have personalized blacklist and whitelist resources, the user group semantic VAD model also improves the user's voice recognition experience.

[0114] Furthermore, to acquire training corpus, we can regularly collect list resources, retaining entries with high repetition rates, and then update the semantic recognition model. Using cloud-based recognition services, we can divide different user groups based on audio attribute analysis results, regularly collect blacklist and whitelist resources for different user groups, and train semantic VAD models that adapt to different accents, ages, and genders. This enhances generalization capabilities and further optimizes the user semantic VAD experience.

[0115] It is understandable that the trained semantic recognition model can be applied to smart device control scenarios such as in-vehicle and smart home devices. Through the personalized semantic VAD system, the problems of premature truncation and long waiting are alleviated, which significantly improves the convenience and comfort of users when using in-vehicle and smart home voice interaction systems.

[0116] In combination with the above embodiments, it can be seen that by performing voice recognition on the first voice information, the target user and voice scenario corresponding to the first voice information are determined; then, based on the voice scenario, the black and white lists corresponding to the target user are configured, and the black and white lists are used to adjust the voice end value; and in response to the triggering of the second voice information, the list resources are updated according to the black and white lists, and the second voice information is the voice information in the subsequent conversation of the first voice information; and then the voice end value corresponding to the second voice information is determined through the updated list resources, so as to recognize the second voice information based on the voice end value. In this way, a personalized voice end value configuration process is realized. Since the black and white lists are configured in combination with the user's voice characteristics, and the list resources are maintained through the black and white lists, the voice end value can be adapted to different users, thereby improving the integrity of the content after the user's voice input and improving the accuracy of voice recognition.

[0117] In order to better implement the above solution of the embodiment of the present application, the following also provides related devices for implementing the above solution. Figure 7 , Figure 7 This is a schematic diagram of the structure of a speech recognition device provided in an embodiment of the present application. The speech recognition device 700 includes:

[0118] The recognition unit 701 is configured to perform speech recognition on the first speech information to determine a target user and a speech scene corresponding to the first speech information;

[0119] A configuration unit 702 is configured to configure a blacklist and whitelist corresponding to the target user based on the voice scenario, wherein the blacklist and whitelist are used to adjust the voice end value;

[0120] The configuration unit 702 is further configured to update the list resource according to the blacklist and whitelist in response to the triggering of the second voice information, where the second voice information is the voice information in the subsequent session of the first voice information;

[0121] The identification unit 701 is further configured to determine a voice end value corresponding to the second voice information by using the updated list resource, and identify the second voice information based on the voice end value.

[0122] Optionally, in some possible implementation manners, the identification unit 701 is configured to perform voice recognition on the first voice information to determine a target user corresponding to the first voice information and a voice scene corresponding to the first voice information; compare the first voice information with voiceprint registration information to determine the target user; perform voice recognition on the first voice information to determine a voice recognition result associated with the target user; and perform semantic discrimination on the voice recognition result to determine the voice scene corresponding to the first voice information.

[0123] Optionally, in some possible implementation manners, the identification unit 701 is configured to, when the voice scene is a truncated scene, perform semantic discrimination on the voice recognition result to determine the voice scene corresponding to the first voice information, acquire first content and second content adjacent to each other in the voice recognition result, and determine completeness information corresponding to the first content and the second content, the completeness information being determined based on content completeness and semantic completeness; and if the completeness information indicates that the first content and the second content are complete in terms of content, and the content associated with the first content and the second content is complete in terms of semantics, it is determined that the voice scene corresponding to the first voice information is the truncated scene.

[0124] Optionally, in some possible implementation manners, the identification unit 701 is configured to, when the voice scene is a long-time waiting scene, perform semantic discrimination on the voice recognition result to determine the voice scene corresponding to the first voice information, and determine execution information in the voice recognition result for triggering execution of an action; and if the execution information indicates complete semantics, and the action corresponding to the execution information has not been executed, it is determined that the voice scene corresponding to the first voice information is the long-time waiting scene.

[0125] Optionally, in some possible implementation manners, the identification unit 701 is configured to, when the execution information indicates complete semantics and indicates a target action, if the execution information indicates complete semantics and indicates a target action, it is determined that the voice scene corresponding to the first voice information is the long-time waiting scene, generate prompt content indicating a response time length, and determine, in response to a confirmation operation of a user on the prompt content, that the voice scene corresponding to the first voice information is the long-time waiting scene.

[0126] Optionally, in some possible implementations, the configuration unit 702 is used to, when configuring the blacklist and whitelist corresponding to the target user based on the voice scene, obtain semantically incomplete voice recognition content in the truncated scene, and configure the semantically incomplete voice recognition content to the blacklist in the blacklist and whitelist corresponding to the target user if the voice scene is a truncated scene; if the voice scene is a long-waiting scene, obtain semantically complete voice recognition content in the long-waiting scene, and configure the semantically complete voice recognition content to the whitelist in the blacklist and whitelist corresponding to the target user.

[0127] Optionally, in some possible implementations, the configuration unit 702 is used to send the black and white list to the cloud in response to the triggering of the second voice information when the list resources are updated according to the black and white list, perform voiceprint recognition on the second voice information in the cloud to determine the target user, and load the list resources corresponding to the target user; and update the list resources corresponding to the target user according to the black and white list.

[0128] Optionally, in some possible implementations, the recognition unit 701 is used to match the voice content corresponding to the second voice information with the updated list resource to obtain list hit information when determining the voice end value corresponding to the second voice information through the updated list resource to recognize the second voice information based on the voice end value; determine the hit scenario indicated by the list hit information; and determine the voice end value corresponding to the second voice information based on the hit scenario to recognize the second voice information based on the voice end value.

[0129] Optionally, in some possible implementations, the recognition unit 701 is also used to determine the user group associated with the target user corresponding to the second voice information if the list hit information indicates that there is no hit scenario matching the second voice information in the list resource; obtain the semantic recognition model corresponding to the user group; and determine the voice end value corresponding to the second voice information based on the list resource corresponding to the user group, so as to recognize the second voice information based on the voice end value.

[0130] Optionally, in some possible implementations, the recognition unit 701 is further used to obtain training corpus; classify the training corpus according to preset dimensions based on user group type to obtain training data corresponding to each user group, where the preset dimensions are set based on the language type dimension and the object description dimension; and train the semantic recognition model corresponding to each user group based on the training data.

[0131] By performing voice recognition on the first voice message, the target user and voice scenario corresponding to the first voice message are determined; then, based on the voice scenario, a blacklist and whitelist corresponding to the target user are configured, and the blacklist and whitelist are used to adjust the voice end value; and in response to the triggering of the second voice message, the list resource is updated according to the blacklist and whitelist, and the second voice message is a voice message in the subsequent conversation of the first voice message; and then, the voice end value corresponding to the second voice message is determined through the updated list resource, so that the second voice message is recognized based on the voice end value. This realizes a personalized voice end value configuration process. Since the blacklist and whitelist configuration is combined with the user's voice characteristics, and the list resource is maintained through the blacklist and whitelist, the voice end value can be adapted to different users, improving the integrity of the content after the user's voice input and improving the accuracy of voice recognition.

[0132] The speech recognition device provided in this embodiment is based on the same concept as the method provided in the above embodiments of this application. It can execute the speech recognition method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects of executing the speech recognition method. For technical details not fully described in this embodiment, please refer to the specific processing content of the speech recognition method provided in the above embodiments of this application, and will not be repeated here.

[0133] The functions implemented by the above identification unit 701 and configuration unit 702 can be implemented by the same or different processors respectively, and this embodiment of the application does not limit this.

[0134] It should be understood that each unit in the above device can be implemented in the form of a processor calling software. For example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, and the memory can be a memory within the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits. The functions of some or all units can be realized by designing the hardware circuits. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units can be realized by designing the logical relationships between the components within the circuit. For another example, the hardware circuit can be implemented by a PLD. For example, an FPGA can include a large number of logic gate circuits. The connection relationships between the logic gate circuits are configured through a configuration file to realize the functions of some or all of the above units. All units of the above device can be implemented entirely in the form of a processor calling software, or entirely in the form of hardware circuits, or partially in the form of a processor calling software, with the remaining part implemented in the form of hardware circuits.

[0135] In the embodiments of the present application, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can implement certain functions through a logic relationship of a hardware circuit, which is fixed or can be reconfigured. For example, the processor is an ASIC or a PLD implemented hardware circuit, such as an FPGA, etc. In the reconfigurable hardware circuit, the processor loads a configuration document to implement the hardware circuit configuration. It can be understood that the processor loads instructions to implement the functions of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, a TPU, a DPU, etc.

[0136] It can be seen that each unit in the above apparatus can be one or more processors (or processing circuits) configured to implement the above methods, such as a CPU, a GPU, an NPU, a TPU, a DPU, a microprocessor, a DSP, an ASIC, an FPGA, or a combination of at least two of these processor forms.

[0137] In addition, each unit in the above apparatus can be integrated together or can be independently implemented. In one implementation, these units are integrated together to implement a SOC. The SOC can include at least one processor for implementing any of the above methods or the functions of the units of the apparatus. The at least one processor can be different, such as including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0138] The embodiments of the present application also propose a speech recognition device; the speech recognition device includes a processor and an input and output component. The input and output component is used to obtain speech information;

[0139] The processor is used to perform the speech recognition method of any one of the above embodiments on the speech information obtained by the input and output component to recognize the speech content.

[0140] The above interface circuit can be any interface circuit capable of realizing data communication function, such as USB interface circuit, Type-C interface circuit, serial port circuit, PCIE circuit, etc.

[0141] Optionally, the embodiments of the present application also provide a system, an interactive client is used to obtain speech information and send the speech information to the server, and display the speech recognition result output by the server;

[0142] The server is configured to perform voice content recognition on the voice information acquired by the interactive client by executing the voice recognition method described in any one of the above embodiments.

[0143] Another embodiment of the present application also provides a voice recognition device, see Figure 8 As shown, the device includes:

[0144] Memory 800 and processor 810;

[0145] The memory 800 is connected to the processor 810 and is used to store programs;

[0146] The processor 810 is configured to implement the speech recognition method disclosed in any of the above embodiments by running the program stored in the memory 800 .

[0147] Specifically, the above-mentioned speech recognition device may further include: a bus, a communication interface 820 , an input device 830 and an output device 840 .

[0148] The processor 810, the memory 800, the communication interface 820, the input device 830 and the output device 840 are interconnected via a bus.

[0149] A bus may include a pathway that transfers information between components of a computer system.

[0150] Processor 810 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, or the like, or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components.

[0151] The processor 810 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0152] The memory 800 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include program code, and the program code includes computer operating instructions. More specifically, the memory 800 may include read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), other types of dynamic storage devices that can store information and instructions, disk storage, flash, etc.

[0153] The input device 830 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0154] Output device 840 may include devices that allow information to be output to a user, such as a display screen, printer, speakers, etc.

[0155] The communication interface 820 may include any transceiver or similar device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0156] The processor 810 executes the program stored in the memory 800 and calls other devices, which can be used to implement the various steps of any speech recognition method provided in the above embodiments of the present application.

[0157] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the speech recognition method according to various embodiments of the present application described in any of the above-mentioned embodiments of this specification.

[0158] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0159] In addition, an embodiment of the present application may also be a storage medium having a computer program stored thereon, and the computer program is used by a processor to execute the steps of the speech recognition method according to various embodiments of the present application described in any of the above embodiments of this specification, specifically the following steps:

[0160] Performing voice recognition on the first voice information to determine a target user and a voice scenario corresponding to the first voice information;

[0161] Configuring a blacklist and whitelist corresponding to the target user based on the voice scenario, wherein the blacklist and whitelist is used to adjust the voice end value;

[0162] In response to a trigger of a second voice message, updating the list resource according to the blacklist and whitelist, wherein the second voice message is a voice message in a subsequent conversation of the first voice message;

[0163] The voice end value corresponding to the second voice information is determined through the updated list resource, so as to recognize the second voice information based on the voice end value.

[0164] For the sake of simplicity, the aforementioned method embodiments are described as a series of action combinations. However, those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0165] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similarities between the various embodiments can be referred to in conjunction with each other. For device embodiments, since they are generally similar to method embodiments, their description is relatively simple, and for relevant details, reference can be made to the description of the method embodiments.

[0166] The steps in the methods of each embodiment of the present application can be adjusted in sequence, merged, and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0167] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be merged, divided, and deleted according to actual needs.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or submodules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, which can be electrical, mechanical or other forms.

[0169] The modules or submodules described as separate components may or may not be physically separate, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules may be selected to achieve the purpose of this embodiment according to actual needs.

[0170] In addition, each functional module or submodule in each embodiment of the present application may be integrated into a processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into a single module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or software functional modules or submodules.

[0171] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0172] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, software units executed by a processor, or a combination of the two. The software units may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0173] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0174] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to those skilled in the art and are within the scope of the application as defined by the appended claims, the general principles defined herein can be applied to other embodiments without departing from the spirit or essential characteristics of the application. Thus, the present application is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A speech recognition method, characterized in that: include: Performing voice recognition on the first voice information to determine a target user and a voice scenario corresponding to the first voice information; The speech scenarios include truncation scenarios and long waiting scenarios; Based on the voice scenario, a blacklist and a whitelist corresponding to the target user are configured, wherein the blacklist and the whitelist are used to adjust the voice end value; the blacklist in the blacklist and the whitelist include the voice recognition content of the truncation scenario; and the whitelist in the blacklist and the whitelist include the voice recognition content of the long waiting scenario; In response to a trigger of a second voice message, updating the list resource according to the blacklist and whitelist, wherein the second voice message is a voice message in a subsequent conversation of the first voice message; determining a voice end value corresponding to the second voice information through the updated list resource, so as to recognize the second voice information based on the voice end value; If there is no hit scenario matching the second voice information in the updated list resource, determining a user group associated with the target user corresponding to the second voice information; The voice end value corresponding to the second voice information is determined based on the list resource corresponding to the user group, so as to recognize the second voice information based on the voice end value.

2. The method according to claim 1, characterized in that Performing voice recognition on the first voice information to determine a target user and a voice scenario corresponding to the first voice information includes: Get voiceprint registration information; Comparing the first voice information with the voiceprint registration information to determine the target user; Performing voice recognition on the first voice information to determine a voice recognition result associated with the target user; Perform semantic discrimination on the speech recognition result to determine the speech scene corresponding to the first speech information.

3. The method according to claim 2, characterized in that The speech scene is a truncated scene, and performing semantic discrimination on the speech recognition result to determine the speech scene corresponding to the first speech information includes: Acquire first content and second content that are adjacent to each other in the speech recognition result; Determining integrity information corresponding to the first content and the second content, wherein the integrity information is set based on content integrity and semantic integrity; If the completeness information indicates that the semantics of the first content is incomplete, the semantics of the second content is complete, and the semantically associated content of the first content and the second content is complete, it is determined that the voice scene corresponding to the first voice information is the truncated scene.

4. The method according to claim 2, characterized in that The voice scene is a long-wait scene, and performing semantic discrimination on the voice recognition result to determine the voice scene corresponding to the first voice information includes: Determining execution information in the speech recognition result for triggering execution of an action; If the semantics indicated by the execution information are complete and the action corresponding to the execution information has not been executed, it is determined that the voice scene corresponding to the first voice information is the long waiting scene.

5. The method according to claim 4, characterized in that If the semantics indicated by the execution information are complete and the execution information is used to indicate a target action, determining that the voice scene corresponding to the first voice information is the long-wait scene includes: If the semantics indicated by the execution information are complete and the execution information is used to indicate the target action, then generating prompt content indicating the response time; In response to a user confirmation operation on the prompt content, it is determined that the voice scene corresponding to the first voice information is the long-wait scene.

6. The method according to claim 1, characterized in that The configuring the blacklist and whitelist corresponding to the target user based on the voice scenario includes: If the voice scene is a truncated scene, obtaining semantically incomplete voice recognition content in the truncated scene, and configuring the semantically incomplete voice recognition content to a blacklist in the blacklist and whitelist corresponding to the target user; If the voice scene is a long-wait scene, semantically complete voice recognition content in the long-wait scene is obtained, and the semantically complete voice recognition content is configured into a whitelist in the blacklist and whitelist corresponding to the target user.

7. The method according to claim 1, characterized in that The updating of the list resource according to the blacklist and whitelist in response to the triggering of the second voice information includes: In response to the triggering of the second voice message, the blacklist and whitelist are sent to the cloud, the second voice message is subjected to voiceprint recognition in the cloud to determine the target user, and the list resource corresponding to the target user is loaded; The list resource corresponding to the target user is updated according to the blacklist and whitelist.

8. The method according to claim 1, characterized in that The determining the voice end value corresponding to the second voice information by using the updated list resource, so as to recognize the second voice information based on the voice end value, includes: Matching the voice content corresponding to the second voice information with the updated list resource to obtain list hit information; determining a hit scenario indicated by the list hit information; The voice end value corresponding to the second voice information is determined based on the hit scenario, so as to recognize the second voice information based on the voice end value.

9. A speech recognition device, characterized in that: include: a recognition unit, configured to perform speech recognition on the first speech information to determine a target user and a speech scenario corresponding to the first speech information; The speech scenarios include truncation scenarios and long waiting scenarios; A configuration unit is configured to configure a blacklist and whitelist corresponding to the target user based on the voice scenario, wherein the blacklist and whitelist are used to adjust the voice end value; the blacklist in the blacklist and whitelist includes the voice recognition content of the truncation scenario; and the whitelist in the blacklist and whitelist includes the voice recognition content of the long waiting scenario; The configuration unit is further configured to update the list resource according to the blacklist and whitelist in response to a trigger of a second voice message, wherein the second voice message is a voice message in a subsequent session of the first voice message; The recognition unit is further configured to determine a voice end value corresponding to the second voice information through the updated list resource, so as to recognize the second voice information based on the voice end value; The recognition unit is also used to determine the user group associated with the target user corresponding to the second voice information if there is no hit scenario matching the second voice information in the updated list resource; determine the voice end value corresponding to the second voice information based on the list resource corresponding to the user group, so as to recognize the second voice information based on the voice end value.

10. A speech recognition device comprising an input and output component and a processor; The input and output components are used to obtain voice information; The processor is configured to perform voice content recognition on the voice information acquired by the input and output component by executing the voice recognition method according to any one of claims 1 to 8.

11. A speech recognition system, comprising an interactive client and a server; The interactive client is used to obtain voice information and send the voice information to the server, and to display the voice recognition results output by the server; The server is configured to perform voice content recognition on the voice information obtained by the interactive client by executing the voice recognition method according to any one of claims 1 to 8.

12. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the speech recognition method according to any one of claims 1 to 8 is implemented.

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