本书为修订和扩展的新版本，新版里包括更为详细的EM算法处理、有效的近似维特比训练程序描述，和基于n-最佳搜索的困惑测度和多通解码覆盖的理论推导。为了支持对马尔可夫模型理论基础的讨论，还特别强调了实际算法的解决方案。具体来说，本书的特点如下：介绍了马尔可夫模型的形式化框架；涵盖了概率量的鲁棒处理；提出了具体应用领域隐马尔可夫模型的配置方法；描述了高效处理马尔可夫模型的重要方法，以及模型对不同任务的适应性；研究了在复杂解空间中由马尔可夫链和隐马尔可夫模型联合应用而产生的搜索算法；回顾了马尔可夫模型的。

1 Introduction
1.1 Thematic Context
1.2 Functional Principles of Markov Models
1.3 Goal and Structure of the Book
2 Application Areas
2.1 Speech
2.2 Writing
2.3 Biological Sequences
2.4 Outlook
Part I Theory
3 PartFoundations of Mathematical Statistics
3.1 Random Experiment, Event, and Probability
3.2 Random Variables and Probability Distributions
3.3 Parameters of Probability Distributions
3.4 Normal Distributions and Mixture Models
3.5 Stochastic Processes and Markov Chains
3.6 Principles of Parameter Estimation
3.6.1 Maximum Likelihood Estimation
3.6.2 Maximum a Posteriori Estimation
3.7 Bibliographical Remarks
4 PartVector Quantization and Mixture Estimation
4.1 Definition
4.2 Optimality
4.2.1 Nearest-Neighbor Condition
4.2.2 Centroid Condition
4.3 Algorithms for Vector Quantizer Design
4.3.1 Lloyd's Algorithm
4.3.2 LBG Algorithm
4.3.3 k-Means Algorithm
4.4 Estimation of Mixture Density Models
4.4.1 EM Algorithm
4.4.2 EM Algorithm for Gaussian Mixtures
4.5 Bibliographical Remarks
5 Hidden Markov Models
5.1 Definition
5.2 Modeling Outputs
5.3 Use Cases
5.4 Notation
5.5 Evaluation
5.5.1 The Total Output Probability
5.5.2 Forward Algorithm
5.5.3 The Optimal Output Probability
5.6 Decoding
5.6.1 Viterbi Algorithm
5.7 Parameter Estimation
5.7.1 Foundations
5.7.2 Forward-Backward Algorithm
5.7.3 Training Methods
5.7.4 Baum-Welch Algorithm
5.7.5 Viterbi Training
5.7.6 Segmental k-Means Algorithm
5.7.7 Multiple Observation Sequences
5.8 Model Variants
5.8.1 Alternative Algorithms
5.8.2 Alternative Model Architectures
5.9 Bibliographical Remarks
6 n-Gram Models
6.1 Definition
6.2 Use Cases
6.3 Notation
6.4 Evaluation
6.5 Parameter Estimation
6.5.1 Redistribution of Probability Mass
6.5.2 Discounting
6.5.3 Incorporation of More General Distributions
6.5.4 Interpolation
6.5.5 Backing off
6.5.6 Optimization of Generalized Distributions
6.6 Model Variants
6.6.1 Category-Based Models
6.6.2 Longer Temporal Dependencies
6.7 Bibliographical Remarks
Part II Practice
7 Computations with Probabilities
7.1 Logarithmic Probability Representation
7.2 Lower Bounds for Probabilities
7.3 Codebook Evaluation for Semi-continuous HMMs
7.4 Probability Ratios
8 Configuration of Hidden Markov Models
8.1 Model Topologies
8.2 Modularization
8.2.1 Context-Independent Sub-word Units
8.2.2 Context-Dependent Sub-word Units
8.3 Conpound Models
8.4 Profile HMMs
8.5 Modeling Outputs
9 Robust Parameter Estimation
9.1 Feature Optimization
9.1.1 Decorrelation
9.1.2 Principal Component Analysis I
9.1.3 Whitening
9.1.4 Dimensionality Reduction
9.1.5 Principal Component Analysis IⅡ
9.1.6 Linear Discriminant Analysis
9.2 Tying
9.2.1 Sub-model Units
9.2.2 State Tying
9.2.3 Tying in Mixture Models
9.3 Initialization of Parameters
10 Efficient Model Evaluation
10.1 Efficient Evaluation of Mixture Densities
10.2 Efficient Decoding of Hidden Markov Models
10.2.1 Beam Search Algorithm
10.3 Efficient Generation of Recognition Results
10.3.1 First-Best Decoding of Segmentation Units
10.3.2 Algorithms for N-Best Search
10.4 Efficient Parameter Estimation
10.4.1 Forward–Backward Pruning
10.4.2 Segmental Baum-Welch Algorithm
10.4.3 Training of Model Hierarchies
10.5 Tree-Like Model Organization
10.5.1 HMM Prefix Trees
10.5.2 Tree-Like Representa