书籍，介绍wfst在语音识别中的应用教程。讲得十分详细，值得一读。

Springer-Handbook-of-Speech-Processing

c# 用SAPI实现语音识别及文本转换的详细教程 每一步都特别详细 相信看完之后 语音识别这快就没问题了 翻译的可能有些问题

自然语言处理的入门经典书籍，英文版，英文好的可以无障碍阅读。

FUNDAMENTALS OF SPEECH RECOGNITION(语音识别基本原理)》(英文)

Springer Handbook of Speech Processing.pdf 非常不错的语音信号处理文档

voicebox工具箱

经典教材，不用多说：） Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition. Daniel Jurafsky & James H. Martin. Copyright c 2006, All rights reserved. Draft of June 25, 2007. Do not cite without permission.

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。