A speech endpoint detection algorithm based on Bayes minimum error probability
Jianming Wang · Tianjin Gongye Daxue xuebao · 2008
A approach to speech endpoint detection is presented based on the Bayes minimum error probability. The feature of magnitude average and zero cross ratio average in short time are introduced. Then, an adaptive threshold is applied to detect the endpoint of speech by the Bayes minimum error probability policy decision. This method is tested on the speech database which is established by the author and the experimental results have proven its precision on speech endpoint detection compared with the method only using the feature of the magnitude or energy.