Vocal print identification method, electronic device and computer readable storage medium
A voiceprint recognition and computer program technology, applied in the electronic field, can solve the problem of low accuracy of voiceprint recognition and achieve the effect of real-time evaluation of false positive rate
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Example Embodiment
[0056] Example one
[0057] The embodiment of this application provides a voiceprint recognition method, please refer to Figure 1-a , The voiceprint recognition method mainly includes the following steps:
[0058] 101. Acquire voice data to be analyzed;
[0059] The embodiment of the present invention is applied to a voiceprint recognition system. The voiceprint recognition system includes K subsystems, where K is an integer greater than zero. The system architecture of the voiceprint recognition system of the embodiment of the present invention can refer to Figure 1-b .
[0060] Wherein, each subsystem in the voiceprint recognition system may correspond to different types of voiceprint recognition, and the types of voiceprint recognition include: emotion recognition, age recognition, and language recognition. Further, each subsystem may also correspond to each sub-category in a recognition scenario. For example, in speech recognition, a subsystem corresponds to a language (such as ...
Example Embodiment
[0096] Example two
[0097] In the embodiment of the present invention, the error-prone point classifier needs to be constructed, please refer to Figure 1-c Methods include:
[0098] 201. Establish a training database;
[0099] Take the short-term speech data set as the test data set of each subsystem, and mark all misjudged speech segments in the test process as N different labels according to different subsystems, as the training database, where N is an integer greater than zero .
[0100] 202. Extract MFCC Mel frequency cepstrum coefficient characteristics;
[0101] For each short-term speech data in the training database, Mel Frequency Cepstrum Coefficient (MFCC) features are extracted.
[0102] 203. Training overall change matrix;
[0103] According to the extracted MFCC features, the Universal Background Model (UBM) is trained, and the overall change matrix T is trained.
[0104] 204. Obtain the change factor characteristics of the short-term voice data;
[0105] Obtain the change f...
Example Embodiment
[0114] Example three
[0115] The embodiment of the present invention takes a hybrid system for language recognition as an example to describe in detail the voiceprint recognition method in the embodiment of the present invention, including:
[0116] 1. The architecture of the hybrid system for language recognition in the embodiment of the present invention can be referred to Figure 1-b , Each subsystem independently gives probability values of N different languages.
[0117] 2. Let x be a certain input voice, the output of each subsystem is shown in the following table:
[0118] Language code
[0119] P f (L j |x)(i=1,2,...,N) Each subsystem independently gives a certain input speech belonging to a certain language L j (j = 1, 2,..., N), and the sum of all probabilities is also 1, namely:
[0120]
[0121] Arrange the probabilities of all languages given by the K subsystems into a matrix:
[0122]
[0123] 3. For the operation process of executing the dynamic weight sub-module, ...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap