Backend i-vector enhancement method for speaker recognition system

A speaker recognition and speaker technology, applied in the field of speaker recognition, can solve the problems of complex system structure, increase of voice signal processing calculation, etc., to improve competitiveness, enhance real-time recognition, and improve system noise robustness Effect

Active Publication Date: 2017-09-08
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

If this method is simply used as the front-end module of the speaker recognition system, while improving the system performance to a certain extent, it also complicates the system struct...

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  • Backend i-vector enhancement method for speaker recognition system
  • Backend i-vector enhancement method for speaker recognition system
  • Backend i-vector enhancement method for speaker recognition system

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Embodiment Construction

[0031] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing: figure 1 As shown, the technical scheme that the present invention adopts is as follows: a kind of i-vector back-end enhancement method for speaker recognition system based on DNN comprises the following steps: be divided into two stages of training and recognition, and described training step is:

[0032] The first step is to preprocess the speaker's speech signal, including pre-emphasis, endpoint detection, framing, and windowing.

[0033] (1) Pre-emphasis

[0034] Pre-emphasis processing is to pass the speech signal through a high-pass filter:

[0035] H(Z)=1-μz -1

[0036] The value of μ in the formula is between 0.9-1.0, we usually take 0.97. The purpose of pre-emphasis is to enhance the high-frequency part, make the spectrum of the signal flat, keep it in the entire frequency band from low frequency to high frequency, and use the same sign...

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Abstract

The invention discloses a backend i-vector enhancement method for a speaker recognition system. The method is based on a deep neural network; and a backend i-vector regression model for the speaker recognition system is built on the basis of the application of the deep neural network in speech enhancement, and a backend feature processor applicable to the speaker recognition system is obtained. Compared with a conventional front-end speech enhancement algorithm, the backend i-vector enhancement method of the invention can improve the anti-noise performance of the speaker recognition system and optimize the structure model of the speaker recognition system, so that the practicability of the speaker recognition system in a noise environment can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of speaker recognition, in particular to a back-end i-vector enhancement method for a speaker recognition system. Background technique [0002] Speaker Recognition (SR), also known as voiceprint recognition, is a biometric authentication technology that uses specific speaker information contained in voice signals to identify the identity of the speaker. In recent years, the introduction of the identity vector (i-vector) speaker modeling method based on factor analysis has significantly improved the performance of the speaker recognition system. Experiments show that in the factor analysis of the speaker's speech, usually the channel subspace will contain the speaker's information. Therefore, i-vector uses a low-dimensional total variable space to represent the speaker subspace and channel subspace, and maps the speaker's voice to this space to obtain a fixed-length vector representation (i.e., i-vector). T...

Claims

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Application Information

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IPC IPC(8): G10L15/02G10L15/06G10L15/07G10L17/02G10L17/04G10L25/18G10L25/30
CPCG10L15/02G10L15/063G10L15/07G10L17/02G10L17/04G10L25/18G10L25/30G10L2015/0635
Inventor 王昕张洪冉李宗晏
Owner NANJING UNIV OF POSTS & TELECOMM
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