A voiceprint recognition method based on 3D convolution neural network

Abstract

The invention discloses a voiceprint recognition method based on 3D convolution neural network, which comprises the following steps: step 1, preprocessing the speech signal. In the process of speech acquisition, there will be more channel noise, which will bring great difficulty to the recognition task, therefore, firstly, the input speech data is denoised by spectral subtraction, that is, noise spectrum estimation is subtracted from the noise speech estimation, so as to obtain the spectrum of pure speech. Here, the channel noise is eliminated, and the channel noise is the noise caused by therecording equipment; all the information about the speaker is completely preserved while the channel noise is removed. Compared with other methods, the spectral subtraction method introduces the leastconstraint conditions, the most direct physical meaning and the small calculation amount, so that the accuracy of recognition can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of deep learning and voice signal processing, in particular to a voiceprint recognition method based on a 3D convolutional neural network. Background technique [0002] With the rapid development of the world's informatization process, information security issues are becoming more and more serious, and the impact is increasing. The issue of "personal privacy and confidentiality" urgently needs to be resolved; and how to accurately and safely determine a person's identity has aroused people's thinking. Traditional security authentication methods such as password or key authentication are usually easy to forget, lose, and steal. Common voiceprint recognition methods, such as the early voiceprint recognition method based on signal processing, use some technical methods in signal processing technology to calculate the parameters of voice data in signal science, and then perform template matching and statistical ...

AI Technical Summary

Problems solved by technology

Common voiceprint recognition methods, such as the early voiceprint recognition method based on signal processing, use some technical methods in signal processing technology to calculate the parameters of voice data in signal science, and then perform template matching and statistical analysis of variance. The data is extremely sensitive, the accuracy rate is very low, and the recognition effect is very unsatisfactory

The recognition method based on the Gaussian mixture model can achieve good results and is simple and flexible, but it requires a large amount of voice data and is very sensitive to channel environmental noise, which cannot meet the requirements of real scenarios.

The existing deep learning neural network-based methods do not consider the context-dependent nature of the speech signal, and the extracted features cannot represent the speaker well, and do not fully utilize the advantages of deep learning

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