关键词: 端点检测, 自相关函数, 过零率, 双门限法

Abstract: The process of speech signal recognition and de-noising is usually used only for speech signal segments, and in some situations, different approaches are need to be adopted to deal with speech segment and noise segment. Correlation function is used to describe the associate degree of the signal value at different times. Due to the randomness of noise, there is a great difference between the correlation function of noise and the correlation function of speech, which can be employed to the endpoint detection of speech. Based on this idea, a method is presented in this paper which uses auto-correlation function. The comparison is also made with the classical method of speech endpoint detection which is the double-threshold decision method based on the short-time average energy and short-time average zero-crossing rate. Experimental results show that the presented methods has higher accuracy, and can achieve better results than the short-time average energy and short-time average zero-crossing rate in low SNR.

Key words: endpoint detection, auto-correlation function, zero-crossing rate, double-threshold