基于顺序统计滤波的实时语音端点检测算法

针对嵌入式语音识别系统,提出了一种高效的实时语音端点检测算法. 算法以子带频谱熵为语音/噪声的区分特征, 首先将每帧语音的频谱划分成若干个子带, 计算出每个子带的频谱熵, 然后把相继若干帧的子带频谱熵经过一组顺序统计滤波器获得每帧的频谱熵, 根据频谱熵的值对输入的语音进行分类. 实验结果表明, 该算法能够有效地区分语音和噪声, 可以显著地提高语音识别系统的性能. 在不同的噪声环境和信噪比条件下具有鲁棒性. 此外, 本文提出的算法计算代价小, 简单易实现, 适合实时嵌入式语音识别系统的应用.

An Order Statistics Filtering-based Real-time Voice Activity Detection Algorithm

In this paper,we propose an effective real-time voice activity detection algorithm.It makes use of the subband spectral entropy as the speech/noise discrimination feature.The speech spectrum is divided into several subbands at first. Then,the spectral entropy of each subband is estimated.We apply order statistics filters(OSF)to a sequence of the subband entropies to obtain the spectral entropy of each frame.The speech/noise classification is based on the spectral entropy.The experimental results show that the proposed algorithm can distinguish speech from noise effectively and improve the performance of automatic speech recognition(ASR)system significantly.It is proved to be robust under various noisy environments and SNR conditions.Moreover,the proposed algorithm is of low computational complexity which is suitable for embedded ASR system application.