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Deep-belief-network-characteristic-vector-based channel-robust voiceprint recognition system

A deep belief network and feature vector technology, applied in the field of human-computer voice interaction, can solve problems such as general performance, achieve good robustness, reduce channel mismatch, and reduce effects.

Inactive Publication Date: 2017-02-22
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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AI Technical Summary

Problems solved by technology

[0004] The voiceprint recognition system based on i-vector technology can better reflect the characteristics of the speaker and is one of the mainstream voiceprint recognition technologies, but its performance is average under the condition of channel mismatch

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Embodiment Construction

[0031] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0032]The channel robust voiceprint recognition system based on the deep belief network feature vector of the present invention considers the use of a large number of corpus of different channels and the corresponding speaker ID number (ID) to carry out supervised training on the deep belief network, so using The feature vector extracted by the trained deep belief network is robust to the channel, thus improving the accuracy of the voiceprint recognition system in the case of channel mismatch. The specific steps are as follows, combined with the attached figure 1 The structural representation of the system of the present invention:

[0033] S01: Speech collection a...

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Abstract

The invention, which belongs to the field of voice signal processing and machine learning, relates to a deep-belief-network-characteristic-vector-based channel-robust voiceprint recognition system comprising a voice acquisition and preprocessing module, an original spectral characteristic extraction module, a deep belief network training module, a speaker voiceprint characteristic vector extraction module, a speaker acoustic model generation module and a speaker identification module. On the basis of voice data from different channels and corresponding speaker identity numbers, a deep belief network is trained in a manner of supervision; and a discrimination ratio is provided to select a deep belief network hidden layer output having an optimal class discrimination property, thereby constructing a speaker voiceprint characteristic vector having channel robustness. Compared with the traditional i-vector-based speaker confirmation system, the provided system has higher voiceprint recognition accuracy on the condition of channel mismatching.

Description

technical field [0001] The invention relates to a channel robust voiceprint recognition system based on deep belief network feature vectors, and belongs to the technical field of human-computer voice interaction. Background technique [0002] Voiceprint recognition technology is a kind of biological verification technology, which uses voice to verify the identity of the speaker, that is, to confirm whether a certain voice is spoken by a designated person. This technology has good convenience and security, and has great application prospects in banking, social security, public security, smart home, mobile payment and other fields. However, in practical applications, the traditional voiceprint recognition system faces the problem of channel mismatch, that is, different mobile devices are used for speaker registration and testing, resulting in a decline in the performance of the voiceprint recognition system and a decline in recognition accuracy. Therefore, in order to solve t...

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Application Information

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IPC IPC(8): G10L17/04G10L17/18G10L17/20
CPCG10L17/04G10L17/18G10L17/20
Inventor 邹月娴王迪松黄艺驰柳俊宏
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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