带噪汉语语音识别的端点检测方法

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。该文利用改进的隐马尔柯夫模型(HMM)进行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 Method of Noisy Chinese Speech Recognition

A major cause of error 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 the paper, modified HMM is used in speech detection to make it adaptive to the change of noise. The experiments show high accurate rates can be obtained.