CN116524935A - Audio registration method and device, storage medium and electronic device - Google Patents
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Abstract
The application discloses an audio registration method, an audio registration device, a storage medium and an electronic device, and relates to the technical field of intelligent home/intelligent families, wherein the audio registration method comprises the following steps: acquiring initial audio data acquired by at least one intelligent device, and screening unregistered audio data from the initial audio data; dividing unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags; registering the first user tag based on at least two types of audio data, and generating a user portrait corresponding to the first user tag, wherein at least one intelligent device executes intelligent operation of user portrait matching corresponding to the first user tag when receiving a control instruction triggered by the first user tag. The method and the device solve the technical problem of low accuracy of audio registration.
Description
Technical Field
The application relates to the technical field of smart home/smart home, in particular to an audio registration method, a storage medium and an electronic device.
Background
With the development of smart home, the user portraits in the existing smart home are usually identified only by using registered voiceprints, namely, the user portraits are formed by processing audio data after the user registers by himself, but the content of the smart home interaction is abandoned for the user who does not register, so that the number of specific members and the user requirements in the home cannot be truly and accurately described. Therefore, the technical problem of poor audio registration accuracy exists.
Disclosure of Invention
The embodiment of the application provides an audio registration method, an audio registration device, a storage medium and electronic equipment, so as to at least solve the technical problem of low audio registration efficiency.
According to an aspect of the embodiments of the present application, there is provided an audio registration method, including:
acquiring initial audio data acquired by at least one intelligent device, and screening unregistered audio data from the initial audio data;
dividing the unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags;
And registering the first user tag based on the at least two types of audio data, and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
According to another aspect of the embodiments of the present application, there is also provided an audio registration apparatus, including:
the first acquisition unit is used for acquiring initial audio data acquired by at least one intelligent device and screening unregistered audio data from the initial audio data;
the first processing unit is used for dividing the unregistered audio data into at least two types of audio data according to the audio similarity among the voice data in the unregistered audio data, wherein each type of voice data in the at least two types of voice data corresponds to different unregistered first user labels;
the first registration unit is used for registering the first user tag based on the at least two types of audio data and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
As an alternative, the first processing unit includes:
the accumulation module is used for accumulating the unregistered audio data;
the classifying module is used for classifying the unregistered audio data according to gender and age to obtain audio sub-data under a plurality of different categories under the condition that the data amount of the unregistered audio data reaches a preset threshold value;
and the input module is used for inputting the audio sub-data into the clustering model to obtain at least two types of audio data.
As an alternative, the first input module includes:
the first determining submodule is used for determining K initial clustering centers according to the feature labels corresponding to different categories;
the second determining sub-module is used for determining the minimum Euclidean distance between each audio sub-data and the initial clustering center;
a third determining sub-module, configured to determine an average minimum euclidean distance value of the audio data, where the average minimum euclidean distance value of the audio data is obtained by averaging a certain number of minimum euclidean distance values of the audio sub-data;
and the fourth determining submodule is used for determining that the training of the clustering model is finished under the condition that the average minimum Euclidean distance value is smaller than or equal to a preset threshold value.
As an alternative, the apparatus further includes:
the first input sub-module is used for responding to a first audio data acquisition instruction after the training of the clustering model is completed, inputting the first audio data into the trained clustering model to obtain a first audio recognition result, wherein the first audio data is new audio data acquired by equipment;
and a fifth determining sub-module, configured to determine that the first audio data completes audio registration.
As an alternative, the fifth determining submodule includes:
the scoring subunit is used for scoring the tone quality of the first audio data to obtain a first score result;
and the processing subunit is used for adding a first identity identifier to the first audio data under the condition that the first score result is greater than or equal to a preset threshold value.
As an alternative, the fifth determining submodule includes:
an acquisition subunit, configured to acquire a pre-stored identity list;
a hit subunit, configured to hit the first identity identifier with the identity identifier list;
an updating subunit, configured to update, when the first identity hits a second identity in the identity list, audio data corresponding to the hit second identity into audio data corresponding to the first identity;
An adding subunit, configured to add the first identity to the identity list when the first identity is not in the second identity in the identity list.
As an alternative, the apparatus further includes:
the identifying unit is used for responding to an audio acquisition request after acquiring initial audio data acquired by at least one intelligent device, and identifying characteristic information of target audio, wherein the audio acquisition request is used for requesting to acquire the target audio;
the second unit processing unit is used for determining a first identity tag and a second identity tag of the target audio based on the characteristic information of the target audio after acquiring initial audio data acquired by at least one intelligent device, wherein the first identity tag is used for indicating behavior information corresponding to the target audio, and the second identity tag is used for indicating registration information corresponding to the target audio;
the third processing unit is used for comparing the similarity between the first identity tag and the second identity tag after acquiring the initial audio data acquired by at least one intelligent device to obtain a comparison result;
The second registration unit is used for updating the registration information of the target audio under the condition that the comparison result is greater than or equal to a preset threshold value after the initial audio data acquired by at least one intelligent device are acquired;
and the third registration unit is used for acquiring behavior information corresponding to the first identity tag under the condition that the comparison result is smaller than the preset threshold after acquiring the initial audio data acquired by at least one intelligent device, and completing registration of a new user by utilizing the behavior information.
According to still another aspect of the embodiments of the present application, there is provided a computer-readable storage medium including a stored program.
According to still another aspect of the embodiments of the present application, there is provided an electronic device including a memory and a processor, wherein the memory stores a computer program.
In the embodiment of the application, initial audio data acquired by at least one intelligent device are acquired, and unregistered audio data are screened out from the initial audio data; dividing the unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags; based on the at least two types of audio data, registering the first user tag and generating a user portrait corresponding to the first user tag, wherein when the at least one intelligent device receives a control instruction triggered by the first user tag, the intelligent operation of matching the user portrait corresponding to the first user tag is executed, and the user portrait is registered based on different classifications by clustering processing through utilizing the audio similarity between unregistered audio data, so that the purpose of automatically performing unregistered voiceprint recognition is achieved, the accuracy of audio registration is improved, and the technical problem of poor audio registration accuracy is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a hardware environment of an interaction method of a smart device according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative audio registration method according to an embodiment of the present application;
FIG. 3 is an example schematic diagram of another alternative audio registration method according to an embodiment of the present application;
FIG. 4 is an example schematic diagram of another alternative audio registration method according to an embodiment of the present application;
FIG. 5 is an example schematic diagram of another alternative audio registration method according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an alternative audio registration method apparatus according to an embodiment of the present application;
Fig. 7 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and the accompanying drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to one aspect of the embodiment of the application, an interaction method of intelligent home equipment is provided. The interaction method of the intelligent household equipment is widely applied to full-house intelligent digital control application scenes such as intelligent Home (Smart Home), intelligent Home, intelligent household equipment ecology, intelligent Home (intelligent house) ecology and the like. Alternatively, in this embodiment, the interaction method of the smart home device may be applied to a hardware environment formed by the terminal device 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal device 102 through a network, and may be used to provide services (such as application services and the like) for a terminal or a client installed on the terminal, a database may be set on the server or independent of the server, for providing data storage services for the server 104, and cloud computing and/or edge computing services may be configured on the server or independent of the server, for providing data computing services for the server 104.
The network may include, but is not limited to, at least one of: wired network, wireless network. The wired network may include, but is not limited to, at least one of: a wide area network, a metropolitan area network, a local area network, a wireless network may include, but is not limited to, at least one of: WIFI (Wireless Fidelity ), bluetooth. The terminal device 102 may not be limited to a PC, a mobile phone, a tablet computer, an intelligent air conditioner, an intelligent smoke machine, an intelligent refrigerator, an intelligent oven, an intelligent cooking range, an intelligent washing machine, an intelligent water heater, an intelligent washing device, an intelligent dish washer, an intelligent projection device, an intelligent television, an intelligent clothes hanger, an intelligent curtain, an intelligent video, an intelligent socket, an intelligent sound box, an intelligent fresh air device, an intelligent kitchen and toilet device, an intelligent bathroom device, an intelligent sweeping robot, an intelligent window cleaning robot, an intelligent mopping robot, an intelligent air purifying device, an intelligent steam box, an intelligent microwave oven, an intelligent kitchen appliance, an intelligent purifier, an intelligent water dispenser, an intelligent door lock, and the like.
Optionally, as an alternative embodiment, as shown in fig. 2, the audio registration method includes:
s202, acquiring initial audio data acquired by at least one intelligent device, and screening unregistered audio data from the initial audio data;
s204, dividing unregistered audio data into at least two types of audio data according to the audio similarity among all the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags;
s206, registering the first user tag based on the at least two types of audio data, and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
Optionally, in this embodiment, with development of technology and improvement of living standard of people, more and more families install smart home devices, such as air conditioners, televisions, refrigerators, washing machines and the like capable of performing voice interaction and voice broadcasting, and different members of the families have great differences in use methods and frequencies of the smart devices, so that forming a background user picture for user preference is helpful for optimizing product design and helping to improve user experience, but formation of user portraits requires various types of data of family members, including distinction of voice of each family member, and only determining which content is sent by which user can form user portraits of the user. The user portrayal in the smart home is usually formed by using only registered voiceprints, because the registered audio can only know which speaker has completed the interaction of which writing function, and can only classify which type of user after knowing the specific requirements of the user. According to the embodiment, through clustering voiceprints, a big data platform is helped to complete user-unaware clustering classification on users with unregistered voiceprints, and speaker id of the unregistered voiceprints is obtained; the user portrait is depicted by combining the clustered voiceprint with the registered voiceprint and the unregistered voiceprint, which comprises the following steps: speaker id, sex, age of speaker, and specific interactive contents;
Optionally, in this embodiment, the initial audio data may be, but is not limited to, audio data collected when the smart home device performs audio interaction with the user, and according to the audio similarity between each audio data in the unregistered audio data, the unregistered audio data is divided into at least two types of audio data, which may be, but is not limited to, that after a certain amount of unregistered audio data is accumulated, feature recognition is performed on the unregistered audio data, classification is performed according to classification modes such as age, gender, and the like, similar data are classified together, labels of the types are not concerned when the similar data are specifically classified, and the purpose is to aggregate the similar data together to form a cluster. Clustering is an unsupervised learning (Unsupervised Learning) method, classification (Classification): the data is divided into different data, wherein the process is to obtain a classifier through training a data set, then predict unknown data through the classifier, and the classification is a supervised learning (Supervised Learning) method.
Optionally, in this embodiment, at least two types of first user tags corresponding to different types of audio data are obtained, where the first user tags that are not registered may be understood as first user tags corresponding to audio data that are not registered in advance by the user, the user image may be understood as a target user model that is built on real data, and the method is an effective tool for outlining a target user and contacting a user requirement, and the building of the user image may include, but is not limited to: collecting user behavior data, serializing and storing the data in an original database, gathering and cleaning the data, analyzing and judging the data based on classification and clustering, finally performing visual construction of a user portrait model, knowing and grasping personal preference and behavior habit of a user, and executing intelligent operation of user portrait matching corresponding to the user under the condition of receiving a control instruction triggered by the user.
For further illustration, for example, as shown in fig. 3, the intelligent air conditioner collects a plurality of pieces of initial audio data 302, the initial audio data 302 is obtained by clustering the initial audio data 302 for "turn on the air conditioner", "i want to turn on the air conditioner", and two types of audio data of children and adults are obtained by classifying according to ages, wherein the two types of audio data correspond to different unregistered first user tags, the first user tags are registered based on at least two types of audio data, a user portrait 304 corresponding to the first user tags is generated, and when a control instruction triggered by the first user tags is received, an intelligent operation of matching the user portrait 304 corresponding to the first user tags is executed.
In the embodiment of the application, initial audio data acquired by at least one intelligent device are acquired, and unregistered audio data are screened out from the initial audio data; dividing the unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags; based on the at least two types of audio data, registering the first user tag and generating a user portrait corresponding to the first user tag, wherein when the at least one intelligent device receives a control instruction triggered by the first user tag, the intelligent operation of matching the user portrait corresponding to the first user tag is executed, and the user portrait is registered based on different classifications by clustering processing through utilizing the audio similarity between unregistered audio data, so that the purpose of automatically performing unregistered voiceprint recognition is achieved, the accuracy of audio registration is improved, and the technical problem of poor audio registration accuracy is solved.
As an alternative, the classifying the unregistered audio data into at least two types of audio data according to the audio similarity between the individual audio data in the unregistered audio data includes:
accumulating the unregistered audio data;
classifying the unregistered audio data according to gender and age under the condition that the data amount of the unregistered audio data reaches a preset threshold value to obtain audio sub-data under a plurality of different categories;
and inputting the audio sub-data into a clustering model to obtain at least two types of audio data.
Alternatively, in this embodiment, it may be understood, but not limited to, that the number of unregistered audio data is accumulated, and if the accumulated unregistered audio data reaches a certain preset threshold, the unregistered audio data is classified according to gender and age, so as to obtain a plurality of audio sub-data in different categories, where the classification criteria are not limited to the age and gender, and may include, but not limited to: based on different languages, the accuracy and the flexibility of the generation of the clustering model can be further improved.
By the embodiment provided by the application, the unregistered audio data is accumulated; classifying the unregistered audio data according to gender and age under the condition that the unregistered audio data reaches a preset threshold value to obtain audio sub-data in a plurality of different categories; and inputting the audio sub-data into a clustering model to obtain at least two types of audio data, thereby realizing the improvement of the accuracy of tone selection.
As an optional solution, inputting the audio sub-data into a clustering model, and obtaining at least two types of audio data includes:
determining K initial clustering centers according to the feature labels corresponding to different categories;
determining a minimum Euclidean distance between each audio sub-data and the initial cluster center;
determining an average minimum Euclidean distance value of the audio data, wherein the average minimum Euclidean distance value of the audio data is obtained by averaging a certain number of minimum Euclidean distance values of the audio sub-data;
and under the condition that the average minimum Euclidean distance value is smaller than or equal to a preset threshold value, determining that the cluster model training is completed.
According to the embodiment provided by the application, K initial clustering centers are determined according to the feature labels corresponding to different categories; determining a minimum Euclidean distance between each audio sub-data and the initial cluster center; determining an average minimum Euclidean distance value of the audio data, wherein the average minimum Euclidean distance value of the audio data is obtained by averaging a certain number of minimum Euclidean distance values of the audio sub-data; and under the condition that the average minimum Euclidean distance value is smaller than or equal to a preset threshold value, determining that the training of the clustering model is completed, and realizing the accuracy of generating the clustering model.
As an alternative, after the training of the determined cluster model is completed, the method further includes:
responding to a first audio data acquisition instruction, inputting the first audio data into the clustering model after training is completed, and obtaining a first audio recognition result, wherein the first audio data is new audio data acquired by equipment;
determining that the first audio data completes audio registration.
Optionally, in this embodiment, it may be understood, but not limited to, that after the clustering model is completed, when a piece of user audio is newly entered, that is, in response to a first audio data acquisition instruction, feature extraction is performed on the piece of user audio, the feature extraction is performed on the piece of user audio to obtain first audio data, the first audio data is input into the trained clustering model, a first audio recognition result is obtained, and it is determined that audio registration is completed for a user portrait corresponding to the first audio data according to an indication of the first audio recognition result.
According to the embodiment provided by the application, the first audio data are input into the clustering model after training is completed in response to a first audio data acquisition instruction, so that a first audio recognition result is obtained, and the first audio data are new audio data acquired by equipment; and determining that the first audio data completes audio registration, thereby realizing the technical effect of improving the accuracy of tone selection.
As an alternative, the determining that the first audio data completes the audio registration includes:
performing tone quality scoring on the first audio data to obtain a first score result;
and under the condition that the first score result is greater than or equal to a preset threshold value, a first identity identifier is given to the first audio data.
For further illustration, as shown in fig. 4, for example, the collected audio may be evaluated and verified in an audio manner, the first audio data may be scored in terms of sound quality, the sound quality is judged by a predetermined threshold, if the obtained first score result is greater than or equal to the predetermined threshold, the audio quality is considered to be better, the confidence corresponding to the first audio data may be obtained in an audio manner, for example, the confidence may be judged to exceed a specified threshold, and if the confidence exceeds the specified threshold, the audio quality is considered to be better, and the speaker is considered to be better.
Optionally, in this embodiment, the first identity identifier may be, but is not limited to, ID information of a speaker, and after the first audio data is obtained and subjected to audio verification, it is determined that the quality of the first audio data is better, and the first audio data ID information may be given to facilitate subsequent comparison and identification of registered voiceprints.
Through the embodiment provided by the application, the tone quality of the first audio data is scored, and a first score result is obtained; and under the condition that the first score result is larger than or equal to a preset threshold value, a first identity identifier is given to the first audio data, so that the accuracy of audio identification is improved.
As an alternative, the determining that the first audio data completes the audio registration includes:
acquiring a pre-stored identity list;
hit the first identity with the identity list;
updating the audio data corresponding to the hit second identity into the audio data corresponding to the first identity under the condition that the first identity hits the second identity in the identity list;
and adding the first identity to an identity list under the condition that the first identity does not hit the second identity in the identity list.
Optionally, in this embodiment, the identification list may, but not limited to, include information such as the gender of the speaker, the audio content corresponding to the age, etc., and the identification list may, but not limited to, record all the registered audio data, and by sorting the identification list, the complete structure of the family, such as the specific number of the family, the proportion of men and women, whether the old and the young, etc., may be further obtained, and by setting the home environment, whether the child mode, the old mode, etc., may be required, so that a more humanized home service may be provided.
Optionally, in this embodiment, the first identity identifier is matched with and hit on the identity identifier list, and the second identity identifier may be, but is not limited to, an audio after registration, and if the first identity identifier hits the registered audio data, it is determined that the audio data and the hit second audio data are the same speaker, so that the audio data corresponding to the second identity identifier is updated to the audio data corresponding to the first identity identifier; and under the condition that the first identity identifier does not hit the second identity identifier in the identity identifier list, judging unregistered audio data when the first audio data corresponding to the first identity identifier is judged, so that the first identity identifier is added into the identity identifier list.
Through the embodiment provided by the application, a pre-stored identity list is obtained;
hit the first identity with the identity list;
updating the audio data corresponding to the hit second identity into the audio data corresponding to the first identity under the condition that the first identity hits the second identity in the identity list;
under the condition that the first identity does not hit the second identity in the identity list, the first identity is added to the identity list, so that the beneficial effect of improving the accuracy of audio data registration is achieved.
As an alternative, the acquiring the initial audio data acquired by the at least one smart device further includes:
identifying characteristic information of target audio in response to an audio acquisition request, wherein the audio acquisition request is used for requesting acquisition of the target audio;
determining a first identity tag and a second identity tag of the target audio based on the characteristic information of the target audio, wherein the first identity tag is used for indicating behavior information corresponding to the target audio, and the second identity tag is used for indicating registration information corresponding to the target audio;
comparing the similarity of the first identity tag with the similarity of the second identity tag to obtain a comparison result;
updating the registration information of the target audio under the condition that the comparison result is larger than or equal to a preset threshold value;
and under the condition that the comparison result is smaller than the preset threshold value, acquiring behavior information corresponding to the first identity tag, and completing registration of a new user by utilizing the behavior information.
According to the embodiment provided by the application, characteristic information of target audio is identified in response to an audio acquisition request, wherein the audio acquisition request is used for requesting acquisition of the target audio;
Determining a first identity tag and a second identity tag of the target audio based on the characteristic information of the target audio, wherein the first identity tag is used for indicating behavior information corresponding to the target audio, and the second identity tag is used for indicating registration information corresponding to the target audio;
comparing the similarity of the first identity tag with the similarity of the second identity tag to obtain a comparison result;
updating the registration information of the target audio under the condition that the comparison result is larger than or equal to a preset threshold value;
and under the condition that the comparison result is smaller than the preset threshold value, acquiring behavior information corresponding to the first identity tag, and finishing registration of a new user by utilizing the behavior information, thereby realizing improvement of the accuracy of audio registration.
For easy understanding, the audio registration method is applied in a specific audio registration scenario:
optionally, in this embodiment, for example, as shown in fig. 5, a certain device (assumed to be a water heater) accumulates audio data of an expected data amount, classifies the audio data according to a recognition result (gender result) of unregistered voiceprints, clusters the voiceprints of the data of each class, and determines whether the clustering is successful; if the clustering is successful, different clustering models are obtained, and each model represents a speaker; when the clustering is successful and new audio is injected, verifying which speaker the new audio belongs to by using a clustering model of the unregistered voiceprint; obtaining score scoring results of the audio through verification; through a pre-given score threshold, if the threshold is exceeded, the audio quality is considered to be good; judging whether the new audio is a registered speaker through a registered voiceprint, obtaining confidence level at the same time, judging whether the confidence level exceeds a specified threshold, and if so, considering that the audio quality is better, and comparing and fitting the speaker; judging whether the speekerid of the unregistered voiceprint pointed by the audio and the speaker of the registered voiceprint are the same speeker or not after the identification of the unregistered voiceprint is met and the verification of the registered voiceprint; if the audio is the same speekerid, the audio needs to be put into a list of updated audio; if the model clustered by the unregistered voice is newly added with the speekerid, the speekerid is used as a new unregistered speaker; generating a new list by audio corresponding to each speekerid, wherein the list contains information such as gender, age, corresponding audio content and the like of a speaker, and finishing the depiction of the user portrait; through the arrangement, the whole structure of the family can be obtained, such as: the number of families, the proportion of men and women, whether the old, the young and the like exist; thus, through the setting of the home environment, whether a child mode, an old man mode and the like are needed or not, more humanized home service can be provided; the embodiment performs clustering voiceprint recognition on the voice data of the home environment, and helps to complete user portraits.
Optionally, in this embodiment, it is determined whether the registered voiceprint and the unregistered voiceprint are the same speeker, and the similarity may be determined by comparing the maps of the existing audio lists, and if the similarity is greater than a specified threshold, the speaker is considered to be the same speaker.
It will be appreciated that in the specific embodiments of the present application, related data such as user information is referred to, and when the above embodiments of the present application are applied to specific products or technologies, user permissions or consents need to be obtained, and the collection, use and processing of related data need to comply with related laws and regulations and standards of related countries and regions.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
According to another aspect of embodiments of the present application, there is also provided an apparatus for implementing audio registration. As shown in fig. 6, the apparatus includes:
a first obtaining unit 602, configured to obtain initial audio data collected by at least one intelligent device, and screen unregistered audio data from the initial audio data;
a first processing unit 604, configured to divide the unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, where each type of audio data in the at least two types of audio data corresponds to a different first user tag that is unregistered;
the first registration unit 606 is configured to register the first user tag based on the at least two types of audio data, and generate a user portrait corresponding to the first user tag, where the at least one intelligent device performs an intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
As an alternative, the first processing unit includes:
the accumulation module is used for accumulating the unregistered audio data;
the classifying module is used for classifying the unregistered audio data according to gender and age to obtain audio sub-data under a plurality of different categories under the condition that the data amount of the unregistered audio data reaches a preset threshold value;
and the input module is used for inputting the audio sub-data into a clustering model to obtain at least two types of audio data.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
As an alternative, the first input module includes:
the first determining submodule is used for determining K initial clustering centers according to the feature labels corresponding to different categories;
a second determining sub-module, configured to determine a minimum euclidean distance between each audio sub-data and the initial cluster center;
a third determining sub-module, configured to determine an average minimum euclidean distance value of the audio data, where the average minimum euclidean distance value of the audio data is obtained by averaging a certain number of minimum euclidean distance values of the audio sub-data;
And the fourth determining submodule is used for determining that the cluster model training is completed under the condition that the average minimum Euclidean distance value is smaller than or equal to a preset threshold value.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
As an alternative, the apparatus further comprises:
the first input sub-module is used for responding to a first audio data acquisition instruction after the cluster model training is completed, inputting the first audio data into the cluster model after the training is completed, and obtaining a first audio recognition result, wherein the first audio data is new audio data acquired by equipment;
and a fifth determining sub-module, configured to determine that the first audio data completes audio registration.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
As an alternative, the fifth determining submodule includes:
the scoring subunit is used for scoring the tone quality of the first audio data to obtain a first score result;
and the processing subunit is used for adding a first identity identifier to the first audio data under the condition that the first score result is greater than or equal to a preset threshold value.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
As an alternative, the fifth determining submodule includes:
an acquisition subunit, configured to acquire a pre-stored identity list;
a hit subunit, configured to hit the first identity identifier with the identity identifier list;
an updating subunit, configured to update, when the first identity hits a second identity in the identity list, audio data corresponding to the hit second identity to audio data corresponding to the first identity;
an adding subunit, configured to add the first identity to an identity list if the first identity does not hit the second identity in the identity list.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
As an alternative, the apparatus further comprises:
the identifying unit is used for responding to an audio acquisition request after acquiring initial audio data acquired by at least one intelligent device, and identifying characteristic information of target audio, wherein the audio acquisition request is used for requesting to acquire the target audio;
The second unit processing unit is used for determining a first identity tag and a second identity tag of the target audio based on the characteristic information of the target audio after acquiring initial audio data acquired by at least one intelligent device, wherein the first identity tag is used for indicating behavior information corresponding to the target audio, and the second identity tag is used for indicating registration information corresponding to the target audio;
the third processing unit is used for comparing the similarity between the first identity tag and the second identity tag after acquiring the initial audio data acquired by at least one intelligent device to obtain a comparison result;
the second registration unit is used for updating the registration information of the target audio after the initial audio data acquired by the at least one intelligent device are acquired, and the comparison result is larger than or equal to a preset threshold value;
and the third registration unit is used for acquiring behavior information corresponding to the first identity tag under the condition that the comparison result is smaller than the preset threshold after acquiring the initial audio data acquired by at least one intelligent device, and completing registration of a new user by utilizing the behavior information.
Specific embodiments may refer to examples shown in the audio registration method, and in this example, details are not described herein.
According to a further aspect of the embodiments of the present application, there is also provided an electronic device for implementing an audio registration method, as shown in fig. 7, the electronic device comprising a memory 702 and a processor 704, the memory 702 having stored therein a computer program, the processor 704 being arranged to perform the steps of any of the method embodiments by the computer program.
Alternatively, in the present embodiment, the electronic device may be located in at least one network device among a plurality of network devices of the computer network.
Alternatively, in the present embodiment, the processor may be arranged to perform the following steps by means of a computer program:
s1, acquiring initial audio data acquired by at least one intelligent device, and screening unregistered audio data from the initial audio data;
s2, dividing the unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags;
and S3, registering the first user tag based on the at least two types of audio data, and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
Alternatively, it will be understood by those skilled in the art that the structure shown in fig. 7 is only schematic, and the electronic device may also be a terminal device such as a smart phone (e.g. an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, and a mobile internet device (Mobile Internet Devices, MID), a PAD, etc. Fig. 7 is not limited to the structure of the electronic device. For example, the electronic device may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 7, or have a different configuration than shown in FIG. 7.
The memory 702 may be used to store software programs and modules, such as program instructions/modules corresponding to the audio registration method and apparatus in the embodiments of the present application, and the processor 704 executes the software programs and modules stored in the memory 702, thereby performing various functional applications and data processing, that is, the implemented audio registration method. The memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the memory 702 may further include memory remotely located relative to the processor 704, which may be connected to the terminal via a network. Examples of networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 702 may be used for storing information such as, but not limited to, initial audio data. As an example, as shown in fig. 7, the memory 702 may include, but is not limited to, a first acquisition unit 602, a first processing unit 604, and a first registration unit 606 in an audio registration apparatus. In addition, other module units in the audio registration device of the virtual model may be included, but are not limited to, and are not described in detail in this example.
Alternatively, specific examples of the network for receiving or transmitting data via one network by the transmission device 706 may include a wired network and a wireless network. In one example, the transmission device 706 includes a network adapter (Network Interface Controller, NIC) that may be connected to other network devices and routers via a network cable to communicate with the internet or a local area network. In one example, the transmission device 706 is a Radio Frequency (RF) module that is configured to communicate wirelessly with the internet.
Furthermore, the electronic device further includes: a display 708 for displaying information such as tone indicating information; and a connection bus 710 for connecting the various module components in the electronic device.
In other embodiments, the terminal device or server may be a node in a distributed system, where the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the plurality of nodes through a network communication. Among them, the nodes may form a Peer-To-Peer (P2P) network, and any type of computing device, such as a server, a terminal, etc., may become a node in the blockchain system by joining the Peer-To-Peer network.
According to one aspect of the present application, a computer program product is provided, comprising a computer program/instructions containing program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. When executed by a central processing unit, performs the various functions provided by the embodiments of the present application.
The embodiment numbers are merely for the purpose of description and do not represent the advantages or disadvantages of the embodiments.
It should be noted that the computer system of the electronic device is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
The computer system includes a central processing unit (Central Processing Unit, CPU) which can execute various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) or a program loaded from a storage section into a random access Memory (Random Access Memory, RAM). In the random access memory, various programs and data required for the system operation are also stored. The CPU, the ROM and the RAM are connected to each other by bus. An Input/Output interface (i.e., I/O interface) is also connected to the bus.
The following components are connected to the input/output interface: an input section including a keyboard, a mouse, etc.; an output section including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like, and a speaker, and the like; a storage section including a hard disk or the like; and a communication section including a network interface card such as a local area network card, a modem, and the like. The communication section performs communication processing via a network such as the internet. The drive is also connected to the input/output interface as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, and the like are mounted on the drive as needed so that a computer program read therefrom is mounted into the storage section as needed.
In particular, according to embodiments of the present application, the processes described in the various method flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The computer program, when executed by a central processing unit, performs the various functions defined in the system of the present application.
According to one aspect of the present application, there is provided a computer-readable storage medium, from which a processor of a computer device reads the computer instructions, the processor executing the computer instructions, such that the computer device performs the methods provided in the various alternative implementations.
Alternatively, in the present embodiment, a computer-readable storage medium may be provided to store a computer program for performing the steps of:
s1, acquiring initial audio data acquired by at least one intelligent device, and screening unregistered audio data from the initial audio data;
s2, dividing the unregistered audio data into at least two types of audio data according to the audio similarity between the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags;
and S3, registering the first user tag based on the at least two types of audio data, and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
Alternatively, in this embodiment, all or part of the steps in the various methods of the embodiments may be implemented by a program for instructing a terminal device to execute, where the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
The embodiment numbers are merely for the purpose of description and do not represent the advantages or disadvantages of the embodiments.
The integrated units in the embodiments may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause one or more computer devices (which may be personal computers, servers or network devices, etc.) to perform all or part of the steps of the methods of the various embodiments of the present application.
In the embodiments of the present application, the descriptions of the embodiments are emphasized, and for a part, which is not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and are merely a logical functional division, and there may be other manners of dividing the apparatus in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. An audio registration method, comprising:
acquiring initial audio data acquired by at least one intelligent device, and screening unregistered audio data from the initial audio data;
dividing the unregistered audio data into at least two types of audio data according to the audio similarity between each audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags;
Registering the first user tag based on the at least two types of audio data, and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
2. The method of claim 1, wherein the classifying the unregistered audio data into at least two types of audio data according to an audio similarity between respective ones of the unregistered audio data comprises:
accumulating the unregistered audio data;
classifying the unregistered audio data according to gender and age under the condition that the data amount of the unregistered audio data reaches a preset threshold value to obtain audio sub-data under a plurality of different categories;
and inputting the audio sub-data into a clustering model to obtain at least two types of audio data.
3. The method of claim 2, wherein inputting the audio sub-data into a clustering model to obtain at least two types of audio data comprises:
determining K initial clustering centers according to the feature labels corresponding to different categories;
Determining a minimum Euclidean distance between each audio sub-data and the initial cluster center;
determining an average minimum Euclidean distance value of the audio data, wherein the average minimum Euclidean distance value of the audio data is obtained by averaging a certain number of minimum Euclidean distance values of the audio sub-data;
and under the condition that the average minimum Euclidean distance value is smaller than or equal to a preset threshold value, determining that the cluster model training is completed.
4. The method of claim 3, wherein after the determining cluster model training is complete, the method further comprises:
responding to a first audio data acquisition instruction, inputting the first audio data into the clustering model after training is completed, and obtaining a first audio recognition result, wherein the first audio data is new audio data acquired by equipment;
determining that the first audio data completes audio registration.
5. The method of claim 4, wherein the determining that the first audio data completes an audio registration comprises:
performing tone quality scoring on the first audio data to obtain a first score result;
and under the condition that the first score result is greater than or equal to a preset threshold value, a first identity identifier is given to the first audio data.
6. The method of claim 4, wherein the determining that the first audio data completes an audio registration comprises:
acquiring a pre-stored identity list;
hit the first identity with the identity list;
updating the audio data corresponding to the hit second identity into the audio data corresponding to the first identity under the condition that the first identity hits the second identity in the identity list;
and adding the first identity to an identity list under the condition that the first identity does not hit the second identity in the identity list.
7. The method of claim 1, wherein after the acquiring the initial audio data collected by the at least one smart device and screening the unregistered audio data from the initial audio data, the method further comprises:
identifying characteristic information of target audio in response to an audio acquisition request, wherein the audio acquisition request is used for requesting acquisition of the target audio;
determining a first identity tag and a second identity tag of the target audio based on the characteristic information of the target audio, wherein the first identity tag is used for indicating behavior information corresponding to the target audio, and the second identity tag is used for indicating registration information corresponding to the target audio;
Comparing the similarity of the first identity tag with the similarity of the second identity tag to obtain a comparison result;
updating the registration information of the target audio under the condition that the comparison result is larger than or equal to a preset threshold value; and under the condition that the comparison result is smaller than the preset threshold value, acquiring behavior information corresponding to the first identity tag, and completing registration of a new user by utilizing the behavior information.
8. An audio registration apparatus, comprising:
the first acquisition unit is used for acquiring initial audio data acquired by at least one intelligent device and screening unregistered audio data from the initial audio data;
the first processing unit is used for dividing the unregistered audio data into at least two types of audio data according to the audio similarity among the audio data in the unregistered audio data, wherein each type of audio data in the at least two types of audio data corresponds to different unregistered first user tags;
the first registration unit is used for registering the first user tag based on the at least two types of audio data and generating a user portrait corresponding to the first user tag, wherein the at least one intelligent device executes intelligent operation of matching the user portrait corresponding to the first user tag when receiving a control instruction triggered by the first user tag.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program when run performs the method of any one of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method according to any of claims 1 to 7 by means of the computer program.
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