1. A data recognition method, comprising:

acquiring data to be identified, wherein the data to be identified comprises at least two subdata;

extracting the characteristics of each subdata to obtain the data characteristics of at least two dimensions of each subdata;

determining a correction weight corresponding to the data to be identified based on correlation among data features of different dimensions corresponding to the data to be identified, wherein the dimension of the correction weight corresponds to the dimension of the data features;

based on the correction weight, performing weighting processing on each data feature to obtain each weighted data feature;

and obtaining the identification result of the data to be identified based on each data feature after the weighting processing.

2. The method of claim 1, wherein the data to be recognized is voice data to be recognized, and the subdata is a frame of data of the voice data to be recognized; the recognition result is a voice recognition result of the voice data to be recognized;

the performing feature extraction on each subdata to obtain the data features of each subdata comprises:

extracting the characteristics of each frame of voice data of the voice data to be recognized to obtain the voice characteristics corresponding to each frame of voice data, wherein the data characteristics are voice characteristics;

the determining the correction weight corresponding to the data to be identified based on the correlation between the data features of different dimensions corresponding to the data to be identified includes:

and obtaining the correction weight corresponding to the voice data to be recognized based on the correlation between the voice features of different dimensionalities corresponding to the voice data to be recognized.

3. The method according to claim 1, wherein the determining the correction weight corresponding to the data to be identified based on the correlation between the data features of different dimensions corresponding to the data to be identified comprises:

aiming at each dimension, obtaining a global feature corresponding to each dimension based on the feature of the same dimension corresponding to each data feature;

determining the weight corresponding to each dimension based on the correlation between the global features corresponding to each dimension;

and obtaining a correction weight corresponding to the data to be identified based on the weight corresponding to each dimension.

4. The method according to claim 3, wherein the obtaining, for each dimension, a global feature corresponding to each dimension based on a feature corresponding to the same dimension for each data feature comprises:

and aiming at each dimension, performing global pooling on the features of the same dimension corresponding to each data feature to obtain the global features corresponding to each dimension.

5. The method according to claim 3, wherein the obtaining, for each dimension, a global feature corresponding to each dimension based on a feature corresponding to the same dimension for each data feature comprises:

and for each dimension, performing global processing on the features of the same dimension corresponding to each data feature through a neural network model to obtain the global features corresponding to each dimension.

6. The method according to claim 1 or 2, wherein the determining the correction weight corresponding to the data to be identified based on the correlation between the data features of different dimensions corresponding to the data to be identified comprises:

and determining the correction weight corresponding to the data to be identified by the compression excitation network according to the correlation among the data characteristics of different dimensions.

7. The method according to claim 6, wherein the compressed excitation network comprises a compression module and an excitation module, and the determining, by the compressed excitation network, the modification weight corresponding to the data to be identified according to the correlation between the data features of different dimensions comprises:

obtaining, by the compression module, for each dimension, a global feature corresponding to each dimension based on a feature of the same dimension corresponding to each data feature;

and determining a correction weight corresponding to the data to be identified based on the correlation between the global features corresponding to each dimension through the excitation module.

8. A data recognition apparatus, the apparatus comprising:

the device comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring data to be identified, and the data to be identified comprises at least two subdata;

the extraction module is used for extracting the characteristics of each subdata to obtain the data characteristics of at least two dimensions of each subdata;

the determining module is used for determining a correction weight corresponding to the data to be identified based on correlation among data features of different dimensions corresponding to the data to be identified, wherein the dimension of the correction weight corresponds to the dimension of the data features;

the weighting module is used for weighting each data characteristic based on the correction weight to obtain each weighted data characteristic;

and the processing module is used for obtaining the identification result of the data to be identified based on each data characteristic after the weighting processing.

9. An electronic device, characterized in that the electronic device comprises:

one or more processors;

a memory;

one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of any of claims 1-7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium is for storing a computer program which, when run on a processor, causes the processor to perform the method of any of claims 1-7.

Background

With the advent of the cloud era, big data has attracted more and more attention. Big data refers to a massive and diversified data set acquired based on the internet. The core of big data is to utilize the value of the data, machine learning is a key technology for utilizing the value of the data, and the machine learning is indispensable for the big data. Conversely, for machine learning, the more data a model can utilize, the more likely it is to improve the accuracy of the model.

The basis of machine learning is feature extraction, and by removing irrelevant data and redundant data, the machine learning efficiency and effect can be improved. How to extract and obtain the characteristics with better expression ability from the data to be processed becomes a problem to be solved.

Disclosure of Invention

The application provides a data identification method, a data identification device and electronic equipment, which can solve the problems in the prior art.

The embodiment of the application provides the following specific technical scheme:

in one aspect, an embodiment of the present application provides a data identification method, where the method includes:

acquiring data to be identified, wherein the data to be identified comprises at least two subdata;

extracting the characteristics of each subdata to obtain the data characteristics of at least two dimensions of each subdata;

determining a correction weight corresponding to the data to be identified based on the correlation between the data features of different dimensionalities corresponding to the data to be identified, wherein the dimensionality of the correction weight corresponds to the dimensionality of the data features;

based on the correction weight, performing weighting processing on each data feature to obtain each weighted data feature;

and obtaining the identification result of the data to be identified based on each data characteristic after the weighting processing.

On the other hand, an embodiment of the present invention further provides a data identification apparatus, where the apparatus includes:

the acquiring module is used for acquiring data to be identified, and the data to be identified comprises at least two subdata;

the extraction module is used for extracting the characteristics of each subdata to obtain the data characteristics of at least two dimensions of each subdata;

the determining module is used for determining correction weights corresponding to the data to be identified based on the correlation among the data features of different dimensionalities corresponding to the data to be identified, wherein the dimensionality of the correction weights corresponds to the dimensionality of the data features;

the weighting module is used for weighting each data characteristic based on the correction weight to obtain each weighted data characteristic;

and the processing module is used for obtaining the identification result of the data to be identified based on each data characteristic after weighting processing.

The embodiment of the invention also provides the electronic equipment, which comprises one or more processors; a memory; one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method as set forth in the first aspect of the present application.

Embodiments of the present invention further provide a computer-readable storage medium, which is used for storing a computer program, and when the computer program runs on a processor, the processor may execute the method as shown in the first aspect of the present application.

The beneficial effect that technical scheme that this application provided brought is:

the application provides a data identification method, a device and electronic equipment, wherein based on the correlation among data characteristics of different dimensions corresponding to data to be identified, correction weights corresponding to the data to be identified are determined, based on the correction weights, weighting processing is carried out on the data characteristics of each subdata of the data to be identified, so that correlation modeling of different dimensions is realized, the data characteristics of different dimensions can be corrected by utilizing the global information of the data to be identified, further, correction of each subdata is realized, the extracted data characteristics have stronger directivity, finally, based on each data characteristic after weighting processing, the identification result of the data to be identified is obtained, and after the data characteristics are corrected, the extracted data characteristics have stronger directivity, so that the context information among different subdata can be fully utilized, and the identification performance is effectively improved, the purpose of improving the performance of the whole network is achieved. In addition, the technical scheme is realized based on correlation modeling of different dimensions, and the method does not need to depend on a channel structure, so that the method is suitable for a sequence modeling task and can process data to be identified in a sequence form containing at least two subdata.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.

Fig. 1 is a schematic flowchart of a data identification method according to an embodiment of the present application;

fig. 2 is a schematic diagram of a processing procedure for processing speech data to be recognized according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a data identification device according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.

The technical scheme provided by the embodiment of the application relates to technologies such as cloud computing, an artificial intelligence computer vision technology, a natural language processing technology, machine learning/deep learning and the like, and is specifically explained by the following embodiment.

Cloud computing (cloud computing) refers to a delivery and use mode of an IT infrastructure, and refers to obtaining required resources in an on-demand and easily-extensible manner through a network; the generalized cloud computing refers to a delivery and use mode of a service, and refers to obtaining a required service in an on-demand and easily-extensible manner through a network. Such services may be IT and software, internet related, or other services. Cloud Computing is a product of development and fusion of traditional computers and Network Technologies, such as Grid Computing (Grid Computing), distributed Computing (distributed Computing), Parallel Computing (Parallel Computing), Utility Computing (Utility Computing), Network Storage (Network Storage Technologies), Virtualization (Virtualization), Load balancing (Load Balance), and the like.

With the development of diversification of internet, real-time data stream and connecting equipment and the promotion of demands of search service, social network, mobile commerce, open collaboration and the like, cloud computing is rapidly developed. Different from the prior parallel distributed computing, the generation of cloud computing can promote the revolutionary change of the whole internet mode and the enterprise management mode in concept.

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Computer Vision technology (CV) Computer Vision is a science for researching how to make a machine "see", and further refers to that a camera and a Computer are used to replace human eyes to perform machine Vision such as identification, tracking and measurement on a target, and further image processing is performed, so that the Computer processing becomes an image more suitable for human eyes to observe or transmitted to an instrument to detect. As a scientific discipline, computer vision research-related theories and techniques attempt to build artificial intelligence systems that can capture information from images or multidimensional data. Computer vision technologies generally include image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content/behavior recognition, three-dimensional object reconstruction, 3D technologies, virtual reality, augmented reality, synchronous positioning, map construction, and other technologies, and also include common biometric technologies such as face recognition and fingerprint recognition.

Key technologies for Speech processing Technology (Speech Technology) are automatic Speech recognition Technology (ASR) and Speech synthesis Technology (TTS), as well as voiceprint recognition Technology. The computer can listen, see, speak and feel, and the development direction of the future human-computer interaction is provided, wherein the voice becomes one of the best viewed human-computer interaction modes in the future.

Natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. Therefore, the research in this field will involve natural language, i.e. the language that people use everyday, so it is closely related to the research of linguistics. Natural language processing techniques typically include text processing, semantic understanding, machine translation, robotic question and answer, knowledge mapping, and the like.

Machine Learning (ML) is a multi-domain cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning.

The execution subject of the technical scheme of the application is computer equipment, including but not limited to a server, a personal computer, a notebook computer, a tablet computer, a smart phone and the like. The computer equipment comprises user equipment and network equipment. User equipment includes but is not limited to computers, smart phones, PADs, etc.; the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a cloud consisting of a large number of computers or network servers for cloud computing, wherein the cloud computing is a kind of distributed computing, and a super virtual computer is composed of a group of loosely coupled computers. The computer equipment can run independently to realize the application, and can also be accessed to the network to realize the application through the interactive operation with other computer equipment in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, etc.

The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

An embodiment of the present application provides a data identification method, where an execution subject of the method may be any electronic device, and optionally may be a server, as shown in fig. 1, where the method includes:

step S101, acquiring data to be identified, wherein the data to be identified comprises at least two subdata;

the data to be recognized may be data in a sequence form composed of at least two subdata, for example, the data to be recognized may include voice data, video data, and the like, the subdata corresponding to the voice data may be each frame of data obtained after framing the voice data, and the subdata corresponding to the video data may be each frame of image data of a video. The data to be identified may also be data in any other sequence form or data that can be divided into sequence forms, which is not limited in this application.

Step S102, extracting the characteristics of each subdata to obtain the data characteristics of at least two dimensions of each subdata;

specifically, the server performs feature extraction on each sub-data, and the extracted features may be used as the data features of each sub-data. Optionally, the data features may be extracted through the neural network model, so as to obtain a higher-dimensional feature output by the neural network model, and the higher-dimensional feature is used as the data feature of each subdata. The Neural network model may specifically include Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Long-Short-Term Memory Networks (LSTM), feed-forward sequential Memory Networks (fed-forward sequential Memory Networks), and the like.

Where a dimension may refer to a number of features, one feature for each dimension. The data feature of at least two dimensions means that the data feature comprises at least two features, and the more dimensions the data feature is, the more abundant the expression of the data feature is. The data characteristics are extracted through the neural network model, and the characteristics with higher dimensionality and stronger expression capability output by the neural network model can be obtained.

Step S103, determining correction weights corresponding to the data to be identified based on the correlation among the data features of different dimensions corresponding to the data to be identified;

wherein the dimension of the correction weight corresponds to the dimension of the data feature. In some embodiments, the dimension of the modifying weight corresponds to the dimension of the data feature, and may be the same as the dimension of the data feature. That is, if the modification weight includes a plurality of weights, the numerical value of the dimension of the modification weight, that is, the number of the plurality of weights included in the modification weight, corresponds to one dimension of the data feature.

In other embodiments, the magnitude of the dimension of the modifying weight may also be greater than or less than the magnitude of the dimension of the data feature, i.e., the number of the plurality of weights included in the modifying weight may be greater than or less than the number of the dimensions of the data feature. This embodiment is not limited to this.

The data features of different dimensions of the data to be identified have correlation therebetween, and the correlation may include a dependency relationship between the data features of different dimensions.

The correction weights corresponding to the data to be identified are determined based on the correlation among the data features of different dimensions, and the correction weights corresponding to the data features of different dimensions may be the same or different. In practical applications, the data features of different dimensions are expressions to the sub-data from different dimensions, and the importance degrees of different dimensions are usually different.

In an embodiment, when the correction weight corresponding to the data to be recognized is determined based on the correlation between the data features of different dimensions corresponding to the data to be recognized, the weight corresponding to the important feature can be made larger, and the weight corresponding to the unimportant feature is made smaller, so that the correction weight for the global situation of the data to be recognized, which is obtained based on the weights corresponding to the data features of all dimensions, can enhance the important data features and suppress the unimportant data features, thereby enabling the extracted features to have stronger directivity, and achieving the purpose of improving the performance of the whole network.

Step S104, based on the corrected weight, carrying out weighting processing on each data characteristic to obtain each data characteristic after weighting processing;

specifically, the server performs weighted calculation on the correction weight of the data feature of each dimension and the corresponding data feature of each dimension to obtain each weighted data feature, and corrects the data feature of each dimension based on the correction weight of the data feature of each dimension, so that the important data feature is enhanced, the unimportant data feature is suppressed, and the directivity of the data feature is enhanced.

According to the technical scheme, the correction weight of the data features of each dimension is determined based on the correlation among the data features of different dimensions corresponding to the data to be identified, the data features of different dimensions can be corrected by utilizing the global information of different dimensions of a sequence formed by at least two subdata in the data to be identified, the correction of different subdata is realized, so that the context information of the data to be identified is fully utilized, and the performance of data identification can be effectively improved.

And step S105, obtaining the identification result of the data to be identified based on each data feature after weighting processing.

And the server is used as the data characteristic of the data to be identified based on the weighted data characteristic of each dimension, and processes the data to be identified according to the specific content and the specific application scene of the data to be identified.

Because the data identification method provided by the embodiment can model the correlation among the data features with different dimensions, and does not need to rely on a channel structure, the network can correct the data features with different dimensions by using the global information of the whole data to be identified, namely the correction weight corresponding to the data to be identified, which is obtained through the correlation among the different data features, so that the directionality of each corrected data feature (namely each data feature after weighting processing) is stronger, and the data identification method is more suitable for sequence modeling tasks such as voice identification, video processing and the like. For example, if the data to be recognized is video data, the image recognition result may be obtained by performing image recognition on the video data to be recognized based on the weighted data features.

According to the data identification method provided by the embodiment of the application, the correction weight corresponding to the data to be identified is determined based on the correlation between the data features of different dimensions corresponding to the data to be identified, and each data feature is subjected to weighting processing based on the correction weight, so that the data features extracted from the data to be identified are corrected, the correlation modeling of different dimensions is realized, the data features of different dimensions can be corrected by using the global information of the data to be identified, the correction of each subdata is further realized, the extracted data features have stronger directivity, and the purpose of improving the performance of the whole network is achieved. Furthermore, since the embodiment of the present application is implemented by modeling correlations of different dimensions, instead of modeling correlations of different channels (channels), and does not need to rely on a channel structure, the embodiment of the present application is not only applicable to networks with channel structures, such as CNN, but also applicable to networks without channel structures, such as DNN, LSTM, FSMN, and the like, and can be applicable to any network structure.

The specific implementation manner of determining the correction weight corresponding to the data to be identified by the server is shown in the following embodiments.

In a possible implementation manner, determining a correction weight corresponding to data to be identified based on correlations between data features of different dimensions corresponding to the data to be identified includes:

aiming at each dimension, obtaining a global feature corresponding to each dimension based on the feature of the same dimension corresponding to each data feature;

determining the weight corresponding to each dimension based on the correlation between the global features corresponding to each dimension;

and obtaining a correction weight corresponding to the data to be identified based on the weight corresponding to each dimension.

In practical application, for each dimension, the server may determine a global feature corresponding to each dimension according to a data feature of the same dimension of each subdata, the global feature is a feature reflecting an overall attribute of the feature of the dimension of each subdata, a weight corresponding to each dimension is determined through correlation between the global features of each dimension, and a correction weight corresponding to the data to be identified is obtained based on the weight corresponding to each dimension, and the correction weight is global to the data to be identified, so that the global information can be effectively utilized, the context information is fully utilized, and the processing performance of the data to be identified can be effectively improved.

In an example, assuming that the data to be identified includes T pieces of sub data, and the dimension of the data feature of each piece of sub data is D, the data feature X of the data to be identified in dimension T × D is as follows:

each row in the matrix represents the data characteristics of one subdata, and each column represents the characteristics of each subdata corresponding to the same dimension. Taking the feature of the first dimension as an example, a in the first column can be taken as₁₁、a₂₁…a_T1Determining the global features of the dimensions, determining the weight corresponding to each dimension according to the correlation between the global features of each dimension after determining the global features of each dimension, and obtaining the correction weight corresponding to the whole data to be identified based on the weight corresponding to each dimension.

In a possible implementation manner, for each dimension, obtaining a global feature corresponding to each dimension based on a feature of the same dimension corresponding to each data feature includes:

and aiming at each dimension, performing global pooling on the features of the same dimension corresponding to each data feature to obtain the global features corresponding to each dimension.

In practical application, a global feature corresponding to one dimension can be obtained through global pooling. Optionally, the weighted average calculation may be performed on the features of the same dimension of each sub-data to obtain the global features of the dimension.

In a possible implementation manner, for each dimension, obtaining a global feature corresponding to each dimension based on a feature of the same dimension corresponding to each data feature includes:

and for each dimension, performing global processing on the features of the same dimension corresponding to each data feature through a neural network model to obtain the global features corresponding to each dimension.

In practical applications, the global features may also be obtained through a neural network model, which may specifically be DNN, LSTM, etc.

Optionally, the global feature may be obtained by DNN, and the T × D dimensional data feature of the data to be identified shown in formula (1) is input into the fully-connected layer of DNN to be processed, so as to obtain the global feature corresponding to each dimension.

Optionally, the global feature may be obtained through an LSTM, each sub-data of the data to be identified is sequentially input to the LSTM for processing, and an output of the LSTM corresponding to the last sub-data is used as the global feature of the data to be identified.

In the embodiment of the application, global processing is performed on the data features of each subdata corresponding to the features of the same dimension to obtain global features corresponding to each dimension, weights corresponding to each dimension are determined based on the correlation between the global features corresponding to each dimension, and then correction weights corresponding to the whole data to be recognized are obtained to correct each subdata of the data to be recognized, so that the data features of different dimensions can be corrected by using global information of the data to be recognized without depending on a channel structure, and the data in a sequence form can be processed.

In a possible implementation manner, determining a correction weight corresponding to data to be identified based on correlations between data features of different dimensions corresponding to the data to be identified includes:

and determining the correction weight corresponding to the data to be identified by the compression excitation network according to the correlation among the data characteristics of different dimensions.

In practical application, data features of different dimensions of each subdata can be input into a compressed Excitation (SE) network, the compressed Excitation network determines the weight of the data features of each dimension according to the correlation between the data features of different dimensions based on the data features of different dimensions, and the correction weight corresponding to the data to be identified is obtained based on the weight of the data features of each dimension.

In a possible implementation manner, the compression excitation network includes a compression module and an excitation module, and the determining, by the compression excitation network, a correction weight corresponding to the data to be identified according to the correlation between the data features of different dimensions includes:

obtaining global features corresponding to each dimension based on the features of the same dimension corresponding to each data feature through a compression module;

and determining a correction weight corresponding to the data to be identified based on the correlation between the global features corresponding to each dimension through an excitation module.

In practical application, the compression excitation network can realize corresponding functions through different modules, global pooling processing is carried out on the features of the same dimensionality corresponding to each data feature through the compression module, and the global features corresponding to each dimensionality are output; inputting the global features corresponding to each dimension into an excitation module, outputting the weight of the data features of each dimension, and obtaining the correction weight corresponding to the data to be identified based on the weight of the data features of each dimension.

In one example, the global feature of one dimension can be obtained by the following equation (2):

wherein z is_dA global feature representing the d dimension; f_sq() The data characteristics of each dimension of each subdata are subjected to global pooling processing through a compression module; t represents the number of sub-data; x is the number of_tdData characteristics of d dimension representing the t-th sub-data.

And the SE network determines the weight of the data characteristic of each dimension based on the global characteristic corresponding to each dimension through an excitation module, and further obtains the correction weight corresponding to the data to be identified.

In an example, the data to be identified includes T pieces of sub data, the dimension of the data feature of each piece of sub data is D, the excitation module includes two fully connected layers, the dimension reduction processing is performed through the first fully connected layer, optionally, the dimension reduction processing may be performed through a reduction coefficient, and the global feature with the dimension of D/r is output, where r is an integer greater than 0 and represents the reduction coefficient. Then, carrying out nonlinear transformation, and optionally, adding a Linear rectification function (ReLU) after the layer to carry out nonlinear transformation; and determining the weight of the data characteristic of each dimension through the second fully-connected layer. Specifically, the output dimension is restored to D dimension, and then a 1 × D-dimension global correction coefficient is finally obtained through an activation function as a correction weight of the data to be identified, where the activation function may be a sigmoid function. The global correction coefficient is obtained by using the data feature changes of different dimensions, the correlation among the data features of different dimensions is used, and meanwhile, the global correction coefficient can play a gating role, so that important data features are enhanced, and unimportant data features are inhibited.

The weight of the data feature for each dimension can be obtained by the following equation (3):

s_d＝F_ex(z_d,W)＝σ(W₂δ(W₁z_d)) (3)

wherein s is_dWeights representing d-dimensional features of the compressed excitation network output; f_ex(z_dW) represents processing of global features in d dimension by an excitation moduleC, processing; w₁Network parameters representing a first layer fully connected layer; δ () represents a linear rectification function; w₂Network parameters representing a second layer fully connected layer; σ () represents an activation function.

Weighting the data characteristics of each dimension by using the weight of the data characteristics of each dimension, and obtaining the weighted data characteristics of each dimension through the following formula (4):

X′＝F_scale(X,S)＝S·X (4)

wherein, X' represents the weighted data characteristics of each dimension; f_scale() A point-by-point function representing the element level; s represents the weight of the data characteristic of each dimension; x represents the data characteristic for each dimension before weighting.

Therefore, in the embodiment, based on the SE network, the original use that needs to be combined with the CNN and modeling of different channel correlations is changed into the correlation modeling of different dimensions, so that the network can utilize the global information of the whole data to be identified to correct the features of different dimensions, enhance the important features and suppress the unimportant features, thereby enabling the extracted data features to have stronger directivity and achieving the purpose of improving the performance of the whole network.

In a possible implementation manner, the data to be recognized is voice data to be recognized, and the subdata is one frame of data of the voice data to be recognized; the recognition result is a voice recognition result of the voice data to be recognized;

performing feature extraction on each subdata to obtain the data features of each subdata, wherein the data features comprise:

performing feature extraction on each frame of voice data of the voice data to be recognized to obtain voice features corresponding to each frame of voice data, wherein the data features are voice features;

determining a correction weight corresponding to the data to be identified based on the correlation between the data features of different dimensions corresponding to the data to be identified, wherein the correction weight comprises the following steps:

and obtaining the correction weight corresponding to the voice data to be recognized based on the correlation between the voice features of different dimensionalities corresponding to the voice data to be recognized.

In practical application, frame processing is carried out on voice data to be recognized to obtain voice data of each frame, feature extraction is carried out on the voice data of each frame, and features of at least two dimensions are extracted to serve as voice features of the voice data of each frame. The speech feature may include a spectral feature of the speech data, such as a Mel-scale Frequency Cepstral Coefficient (MFCC), a Linear Prediction Cepstral Coefficient (LPCC), and the like.

The voice features of different dimensions of each frame of voice data have correlation therebetween, and the correlation may include a dependency relationship between the voice features of different dimensions. The method comprises the steps of determining the weight of the voice feature of each dimension through the correlation among the voice features of different dimensions, obtaining a correction weight corresponding to data to be recognized, performing weighting processing on each voice feature through the correction weight, correcting each frame of voice data by using global information of a whole voice sequence, fully utilizing context information, effectively improving voice recognition performance, modeling the correlation of the voice features of different dimensions, enhancing important features, and inhibiting unimportant features, so that the extracted features have stronger directivity, and the aim of improving the performance of the whole network is fulfilled. In addition, the data recognition method provided by the embodiment of the application is suitable for sequence modeling tasks such as voice recognition and the like, is suitable for any network structure, and is not limited to CNN.

The following describes the data processing procedure of the present invention in detail by using a specific embodiment. The embodiment is only one implementation manner of the technical solution of the present application, and does not represent all implementation manners of the technical solution of the present application.

As shown in fig. 2, in this embodiment, data to be recognized is voice data to be recognized, and based on the data recognition method provided in an embodiment of the present application, a specific process of processing the voice data to be recognized so as to correct data features of different dimensions of the voice data to be recognized is as follows:

performing frame processing on the voice data to be recognized, and performing characteristics on each frame of voice dataExtracting to obtain a voice feature X, wherein the voice feature X comprises the voice feature of T frame voice data, inputting the voice feature X into a neural network model (one of DNN, CNN or LSTM, or other neural network models) for feature extraction, and outputting T × D dimensional featureCharacterizing the dimension T by DThe input compression module performs global pooling processing on a time dimension, and adds and averages data characteristics of the same dimension corresponding to each frame of voice data to obtain global characteristics of 1 × D dimension, so as to obtain global information on the whole voice sequence; inputting the global features into an excitation module, wherein the excitation module models the correlation among different dimensions, the excitation module comprises two fully-connected layers, the global features pass through the first fully-connected layer and are subjected to dimension reduction processing to obtain the global featuresThe global feature of the dimension, where r is an integer greater than 0, represents a reduction coefficient, which acts to reduce the number of model parameters by first reducing the output dimension. Then adding a linear rectification function behind the layer for nonlinear transformation, and obtaining the product after the treatment of the linear rectification functionGlobal features of the dimension; through a second full connection layer, the dimension is restored to D dimension, and the global feature of 1 multiplied by D dimension is obtained; and processing the voice data by an activation function to obtain global features of dimension 1 × D, using the global features as correction weights corresponding to the voice data to be recognized and as global correction coefficients, performing weighting processing, namely correction processing, on the voice features of each dimension of each frame of voice data by using the global correction coefficients, and outputting the processed features X' of dimension T × D. Wherein, the global correction coefficient is obtained by using the feature change of different dimensions, and the data features of different dimensions are utilizedThe correlation of the frame data can play a gating role, and the data characteristics of each dimension of each frame of voice data are corrected, so that the important data characteristics are enhanced, and the unimportant data characteristics are suppressed.

According to the data identification method provided by the embodiment of the application, the correction weight corresponding to the data to be identified is determined based on the correlation among the data features of different dimensions corresponding to the data to be identified, the data features of each subdata of the data to be identified are weighted based on the correction weight so as to realize the correction of each subdata, the identification result of the data to be identified is obtained based on each data feature after weighting, important features are enhanced through the correction weight, unimportant features are inhibited, the extracted features are stronger in directivity, and the purpose of effectively improving the performance of the whole network is achieved. In addition, according to the technical scheme, the data to be recognized in the form of the sequence including at least two subdata can be processed, and the problem that the data in the form of the sequence cannot be processed is solved.

Based on the same principle as the method shown in fig. 1, an embodiment of the present disclosure further provides a data recognition apparatus 30, as shown in fig. 3, where the data recognition apparatus 30 includes:

the acquiring module 31 is configured to acquire data to be identified, where the data to be identified includes at least two subdata;

the extraction module 32 is configured to perform feature extraction on each subdata to obtain data features of at least two dimensions of each subdata;

the determining module 33 is configured to determine a correction weight corresponding to the data to be identified based on correlations between data features of different dimensions corresponding to the data to be identified, where a dimension of the correction weight corresponds to a dimension of the data feature;

the weighting module 34 is configured to perform weighting processing on each data feature based on the correction weight to obtain each data feature after weighting processing;

and the processing module 35 is configured to obtain an identification result of the data to be identified based on each data feature after the weighting processing.

In a possible implementation manner, the data to be recognized is voice data to be recognized, and the subdata is one frame of data of the voice data to be recognized; the recognition result is a voice recognition result of the voice data to be recognized;

an extraction module 32 for:

performing feature extraction on each frame of voice data of the voice data to be recognized to obtain voice features corresponding to each frame of voice data, wherein the data features are voice features;

a determining module 33 configured to:

and obtaining the correction weight corresponding to the voice data to be recognized based on the correlation between the voice features of different dimensionalities corresponding to the voice data to be recognized.

In a possible implementation manner, the determining module 33 is specifically configured to:

aiming at each dimension, obtaining a global feature corresponding to each dimension based on the feature of the same dimension corresponding to each data feature;

determining the weight corresponding to each dimension based on the correlation between the global features corresponding to each dimension;

and obtaining a correction weight corresponding to the data to be identified based on the weight corresponding to each dimension.

In a possible implementation manner, when obtaining the global feature corresponding to each dimension based on the feature corresponding to the same dimension of each data feature, the determining module 33 is configured to:

and aiming at each dimension, performing global pooling on the features of the same dimension corresponding to each data feature to obtain the global features corresponding to each dimension.

In a possible implementation manner, when obtaining the global feature corresponding to each dimension based on the feature corresponding to the same dimension of each data feature, the determining module 33 is configured to:

and for each dimension, performing global processing on the features of the same dimension corresponding to each data feature through a neural network model to obtain the global features corresponding to each dimension.

In a possible implementation manner, the determining module 33 is specifically configured to:

and determining the correction weight corresponding to the data to be identified by the compression excitation network according to the correlation among the data characteristics of different dimensions.

In a possible implementation manner, the compressed excitation network includes a compression module and an excitation module, and the determination module 33 is specifically configured to:

obtaining global features corresponding to each dimension based on the features of the same dimension corresponding to each data feature through a compression module;

and determining a correction weight corresponding to the data to be identified based on the correlation between the global features corresponding to each dimension through an excitation module.

The data identification device of the embodiment of the present disclosure may execute the data identification method corresponding to fig. 1 provided in the embodiment of the present disclosure, and the implementation principle is similar, the actions executed by each module in the data identification device of the embodiment of the present disclosure correspond to the steps in the data identification method of the embodiment of the present disclosure, and for the detailed functional description of each module of the data identification device, reference may be specifically made to the description in the corresponding data identification method shown in the foregoing, and details are not repeated here.

The data identification device provided by the embodiment of the application determines the correction weight corresponding to the data to be identified based on the correlation among the data characteristics of different dimensionalities corresponding to the data to be identified, the data characteristics of each subdata of the data to be identified are weighted, so that the correlation modeling of different dimensions is realized, so that the global information of the data to be identified can be utilized to correct the data characteristics of different dimensions, thereby realizing the correction of each subdata, leading the directivity of the extracted data characteristics to be stronger, finally obtaining the identification result of the data to be identified based on each data characteristic after weighting treatment, after the data characteristics are corrected, the extracted data characteristics have stronger directivity, so that the context information among different subdata can be fully utilized, the identification performance is effectively improved, and the aim of improving the performance of the whole network is fulfilled. In addition, the technical scheme is realized based on correlation modeling of different dimensions, and the method does not need to depend on a channel structure, so that the method is suitable for a sequence modeling task and can process data to be identified in a sequence form containing at least two subdata.

The above embodiment introduces the data recognition apparatus from the perspective of a virtual module, and the following introduces an electronic device from the perspective of an entity module, which is specifically as follows:

an embodiment of the present application provides an electronic device, and as shown in fig. 4, an electronic device 8000 shown in fig. 4 includes: a processor 8001 and memory 8003. Processor 8001 is coupled to memory 8003, such as via bus 8002. Optionally, the electronic device 8000 may also include a transceiver 8004. In addition, the transceiver 8004 is not limited to one in practical applications, and the structure of the electronic device 8000 does not limit the embodiment of the present application.

Processor 8001 may be a CPU, general purpose processor, GPU, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. Processor 8001 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, DSP and microprocessor combinations, and so forth.

Bus 8002 may include a path to transfer information between the aforementioned components. The bus 8002 may be a PCI bus or an EISA bus, etc. The bus 8002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.

Memory 8003 may be, but is not limited to, ROM or other types of static storage devices that can store static information and instructions, RAM or other types of dynamic storage devices that can store information and instructions, EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

The memory 8003 is used for storing application program codes for executing the scheme of the present application, and the execution is controlled by the processor 8001. Processor 8001 is configured to execute application program code stored in memory 8003 to implement what is shown in any of the foregoing method embodiments.

An embodiment of the present application provides an electronic device, where the electronic device includes: one or more processors; a memory; one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs, when executed by the processors, obtaining data to be identified, the data to be identified including at least two sub-data; extracting the characteristics of each subdata to obtain the data characteristics of at least two dimensions of each subdata; determining a correction weight corresponding to the data to be identified based on the correlation between the data features of different dimensionalities corresponding to the data to be identified, wherein the dimensionality of the correction weight corresponds to the dimensionality of the data features; based on the correction weight, performing weighting processing on each data feature to obtain each weighted data feature; and obtaining the identification result of the data to be identified based on each data characteristic after the weighting processing.

The present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program runs on a processor, the processor can execute the corresponding content in the foregoing method embodiments.

According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternative implementations of the data recognition method described above.

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.