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.

HMM隐马尔可夫模式的经典学习C语言源码，对学习hmm模式有非常好的帮助。有效的解决了HMM的三个问题：评估，译码和学习训练。

基于HMM语音识别的C语言实现，里面的代码浅显易懂，且是分模块写的，思路比较清晰

非常全面的HMM工具箱，隐马尔可夫matlab工具

基于HMM的姿势识别，data代码一应俱全。

直接可以添加到MATLAB中的隐马尔科夫工具箱，自带有测试小例子

详细介绍了训练hmm和gmm的EM算法，以及其应用，对利用这些模型的朋友，想了解此算法的最好的资料。

hmm好多算法.hmm好多算法.hmm好多算法.hmm好多算法.hmm好多算法.

java开发隐马尔科夫模型的一个jar包，其他CSDN资源上要积分，为切磋零积分下载，有兴趣可以到博客一起研究http://blog.csdn.net/fjssharpsword

一些关于HMM模型的期刊论文，都是比较好的期刊的，主要内容是与HMM有关的一些识别和异常检测的论文，附加一个HMM最基础算法的代码（C++）。