A Speech Endpoint Detection Method Based on Wavelet Coefficient Variance and Sub-Band Amplitude Variance

Speech endpoint detection is one key technology for speech recognition. The paper proposed two kinds of endpoint detection methods: the algorithm based on the wavelet coefficient variance and the algorithm based on the sub-band average amplitude variance. Speech signal with noise was decomposed by wavelet to investigate the statistic characteristics of wavelet coefficient and sub-band amplitude. Their variances were extracted as feature to make endpoint detection. The first method´s adaptability is better than the second method, but its complexity is higher than the second method. So the synthesized speech end-point detection algorithm that is consisted of above two methods was proposed. It can select a suitable way to make operation according to noise type. Thus it can increase system efficiency and implement endpoint detection. Simulations were made under different signal-to-noise ratios and the results show that this method is efficient to segment noisy speech even at a low signal-to-noise ratio