Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs. Whereas the basic principles underlying HMM-based LVCSR are rather straightforward, the approximations and simplifying assumptions involved in a direct implementation of these principles would result in a system which has poor accuracy and unacceptable sensitivity to changes in operating environment. Thus, the practical application of HMMs in modern systems involves considerable sophistication. The aim of this review is first to present the core architecture of a HMM-based LVCSR system and then describe the various refinements which are needed to achieve state-of-the-art performance. These refinements include feature projection, improved covariance modelling, discriminative parameter estimation, adaptation and normalisation, noise compensation and multi-pass system combination. The review concludes with a case study of LVCSR for Broadcast News and Conversation transcription in order to illustrate the techniques described.

说话人识别的基于MATLAB GUI的界面制作，程序已经经过验证可以实现，里面包含了语音识别的文件库，也可以自己建立文件库进行语音识别，主要用了DTW和VQ的说话人识别。

利用matlab GUI实现了语音信号处理，程序经过测试可以直接执行，如果有所疑问请发送消息到1741321723@qq.com进行咨询。主要用了DTW和HMM的语音识别。

text to speech engine for chinese and english