一种改进的小波能量熵语音端点检测算法

摘要

语音端点检测是语音信号处理的一个重要环节,在低信噪比下,端点检测的准确度和鲁棒性较低.为此,提出一种小波能量熵与基音周期相结合的混合端点检测算法.该算法通过分析语音信号的小波能量和小波能量熵,构造不同语者的小波能量熵端点检测参数,针对不同语者的发音特性运用小波能量熵和基音周期检测语音端点.实验结果表明,在不同噪声背景下,当信噪比为5 dB时,该算法的端点检测平均准确率达到84.375％,相对于小波能量和小波能量熵算法均有明显提高.%Speech endpoint detection is an important step in the processing of speech signal.The accuracy and robustness of endpoint detection are low at low Signal to Noise Ratio (SNR).In order to solve this problem,this paper proposes a new endpoint detection algorithm based on wavelet energy entropy and pitch period.It analyzes the speech signal wavelet energy and wavelet entropy to build wavelet energy entropy endpoint detection parameters for different speaker,and utilizes the pronunciation characteristics of different speakers to detect the speech endpoint by wavelet energy entropy and pitch period.Experimental result shows that at different noise background the average detection accuracy is 84.375％ in 5 dB SNR which is more accurate than the wavelet energy algorithm and wavelet energy entropy algorithm.