A Speech Endpoint Detection Algorithm Based on Maximum of Auto-correlation Function and Amended Threshold-crossing Rate
Hailong Chen · Audio Engineering · 2010
In speech processing, endpoint detection is difficult in noisy environments, especially in the presence of nonstationary noise. The traditional characteristic parameters for the endpoint detection can not be adequately described the characteristics of speech signals, resulting in severe degradation of the effect of endpoint detection in low SNR. So a novel method that maximum of autocorrelation function is combined with the amended zero-crossing rate is presented. Experimental results show that the method can compensating the drawback of the maximum of autocorrelation method and the zero-crossing method so that the performance of the detection is improved.