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The Research of Voice Activity Detection Based on Characters in Noise Environment

Author: ZhaoLiXia
Tutor: ZhaoHuan
School: Hunan University
Course: Computer Science and Technology
Keywords: Voice activity detection Feature Entropy Support Vector Machine
CLC: TN912.3
Type: Master's thesis
Year: 2010
Downloads: 142
Quote: 3
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Abstract


The purpose of the speech endpoint detection is to determine the beginning and end of the speech , from the period of the signal containing speech voice signal processing front-end operations , is widely used in the field of speech enhancement , speech coding , speech recognition . Speech endpoint detection method feature-based and model-based two categories , the model-based approach is more complex , the ability to adapt to the environment , and feature-based approach is relatively simple and has some anti-noise capability , this method requires to find some kind of able to distinguish between voice and noise characteristics of robustness . In this paper, the speech endpoint detection method based on the characteristics of a study . Proposed a new distance-based entropy detection algorithm for the detection algorithm based on spectral entropy at low signal-to-noise ratio under the disadvantage of poor robustness . The algorithm uses the probability density of the robustness of the change of the entropy and cepstrum calculation method , a series of calculations of the pre-treated with a noise signal point cepstral coefficients , cepstral coefficients Euclidean distance , Euclidean distance construct the probability density function of the probability density function from the entropy feature distance entropy Finally, a dual - threshold distinction between speech and noise . This paper proposed a support vector machine - based multi-feature detection algorithm . Seeking SNR noisy signal detection algorithm based on support vector machine modified zero the rate and AMMM three characteristics , three characteristics to form a feature matrix , use some noisy signal of support vector machine training , using the training after support vector machine automatically distinguish between voice and noise . This article experiments with noise signal from the noise of France aurora2.0 library of clean voice and Noisex92 of noise library mixture , and simulation experiments using MATLAB tools , the experimental results show that , the two endpoint detection algorithm proposed in this paper has certain robustness , even at lower signal-to-noise ratio better distinguish between voice and noise .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing
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