基于倒谱特征的带噪语音端点检测

在语音识别系统中产生错误识别的原因之一是端点检测有误差.在高信噪比情况下,正确地确定语音的端点并不困难.然而,大多数实际的语音识别系统需工作在低信噪比情况下,一些常规的端点检测方法,例如基于能量的端点检测方法在噪声环境下不能有效地工作.本文利用倒谱特征来检测语音端点,提出了带噪语音端点检测的两个算法,第一个算法利用倒谱距离代替短时能量作为判决的门限,第二个算法改进了基于隐马尔柯夫模型(HMM)的语音检测以适应噪声的变化,实验结果表明本方法可得到高正确率的带噪语音端点检测.

Endpoint Detection of Noisy Speech Based on Cepstrum

A major cause of errors in automatic speech recognition (ASR) systems is the inaccurate detection of the beginning and ending boundaries of test and reference patterns.Accurate determination of endpoints of speech is not very difficult if the SNR is high.Unfortunately,most practical ASR systems must work with a small SNR,and the conventional speech detection methods based on some simple features such as energy cannot work well in noisy environments.In this paper,cepstrum is used as the feature to detect the voice activity.Two algorithms for endpoint detection of noisy speech signal are proposed.The first one takes the cepstral distance as the decision thresholds instead of short time energy.The second approach modified the HMM based speech detector to make it adaptive to the change of noise.The experiments show high accurate rates can be obtained.