这本模式识别的书非常经典，然而也非常稀罕，很少有人有。我最早读的是第二版，这本书全面覆盖了统计模式识别领域的重要知识点。书中用大量篇幅讲解无监督聚类方法，这一点在模式识别教材中应该是独一无二的，比如Duda的书在这方面只留了一章，处理的也比较简单。另外，本书还有章节专门讲特征抽取、选择，以及模板匹配这些内容，也弥补了Duda教材的不足。第三版增加了一些内容，主要是基于核方法的内容，反映了学界的进展。-Book of this pattern recognition is very classic, however, very rare, very few people have. I first read the second edition, this book comprehensively covers the important points of the field of statistical pattern recognition. Book at great length to explain the unsupervised clustering method, which is unique in pattern recognition textbooks, such as Duda' s book in this regard, leaving only one chapter, is also relatively simple to deal with. In addition, the book there are chapters dedicated speakers feature extraction, selection, and template matching, but also compensate for the lack of materials Duda. The third edition of the increase in some of the content is mainly based on the content of the kernel method, reflecting the academic progress.

A Review of Off-line Handwritten Chinese Character Recognition

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

人脸识别（python）

Face Detection and Recognition: Theory and Practice

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.

这个是稀疏编码应用与人脸识别，有算法的参考文献集matlab代码，供大家学习参考

模式识别第四版答案（pattern recognition fourth edition solution）

用 matlab开发的说话人识别算法。用到了GMM,DTW等分类算法，还用到了MFCC特征抽取算法等

这个说话人识别代码中包含两个例子，都已经实现。读者需按照里面pdf进行仿真。