The Research Of Voice Activity Detection Based On Characters In Noise Environment

The purpose of voice activity detection (VAD) is detecting the beginning and ending points of speech from a signal which contains speech. As a pre-operation of speech signal processing, VAD is very important and has potential applications in the areas of speech enhancement, coding, identification and so on. VAD methods could be divided into two categories:feature-based and model-based. Model-based VAD method is complex and has poor adaptation to environment; Feature-based VAD method which requires finding some robust features to distinguish between voice and noise is relatively simple and has some anti-noise capability. This paper focuses on researching feature-based VAD method.Because of VAD method based on entropy can not work well in noisy environment, we propose a new algorithm based on distance entropy. This algorithm makes use of robustness of cepstral and entropy, change the calculate way of probability density function. We obtain cepstral coefficients of each speech point by a series of operations on noisy signal which have been pre-processed. We can get Euclidean distance according to cepstral coefficients, and then, we generate probability density function by Euclidean distance and construct distance entropy by way of probability density function. Finally, we can find useful parts of noisy speech by distance entropy.In addition, we propose another improved algorithm called VAD algorithm based on support vector machine by multi-feature. This algorithm extracts SNR, amending zero crossing rate and AMMM three characteristics from noisy signal, the three characteristics format a characteristic matrix. We employ parts of the noisy signal to train the support vector machine, set parameters, then support vector machine can distinguish noise from speech automatically.Noisy signals used in experiments are mixed by clean speech and noise. Clean speech come from French aurora2.0 Library, and noise come from Noisex92 noise library. Experiments tool is MATLAB. Simulation results prove that the two algorithms proposed in this paper perform well on anti-noise, they can work well in high-noisy environment.