We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our model is motivated by the complex multi-scale structure which appears
HTK, Hidden Markov Model Toolkit. HTK source code, ver 3.4.1 (tar+gzip archive), for Linux/Unix. HTK was originally developed at the Cambridge University Engineering Department (CUED).
HTK Hidden Markov Model Toolkit 隐马尔科夫模型源码，ver 3.4 .1, for windows..1 HTK was (and is) a set of C library modules and tools that was initially used for speech recognition research (using continuous density HMMs) within the Speech Vision and Robotics
This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sour
静态模型在推荐系统中往往将用户的兴趣偏好看作是固定不变的，而在一定程度上与实际并不符合。为此，基于隐Markov动态模型提出一种融合停留时间的类时齐隐Markov个性化推荐模型（ctqHMM）。该模型用隐含状态变量的转移来模拟 Web 用户的兴趣变迁，并用停留时间来描述用户对某一偏好感兴趣的程度和所推荐页面的重要性。然后，提出一种基于该模型平稳分布的用户聚类方法，并将其用于推荐系统中。在真实的 Web服务器访问记录数据上的实验证明，类时齐隐Markov模型具有更好的推荐性能。
