[
-
内容大纲
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。 -
作者介绍
-
目录
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 Representation for n-Gram Models
11 Model Adaptation
11.1 Basic Principles
11.2 Adaptation of Hidden Markov Models
11.2.1 Maximum-Likelihood Linear-Regression
11.3 Adaptation of n-Gram Models
11.3.1 Cache Models
11.3.2 Dialog-Step Dependent Models
11.3.3 Topic-Based Language Models
12 Integrated Search Methods
12.1 HMM Networks
12.2 Multi-pass Search
12.3 Search Space Copies
12.3.1 Context-Based Search Space Copies
12.3.2 Time-Based Search Space Copies
12.3.3 Language-Model Look-Ahead
12.4 Time~Synchronous Parallel Model Decoding
12.4.1 Generation of Segment Hypotheses
12.4.2 Language-Model-Based Search
Part III Systems
13 Speech Recognition
13.1 Recognition System of RWTH Aachen University
13.1.1 Feature Extraction
13.1.2 Acoustic Modeling
13.1.3 Language Modeling
13.1.4 Search
13.2 BBN Speech Recognizer BYBLOS
13.2.1 Feature Extraction
13.2.2 Acoustic Modeling
13.2.3 Language Modeling
13.2.4 Search
13.3 ESMERALDA
13.3.1 Feature Extraction
13.3.2 Acoustic Modeling
13.3.3 Statistical and Declarative Language Modeling
13.3.4 Incremental Search
14 Handwriting Recognition
14.1 Recognition System by BBN
14.1.1 Preprocessing
14.1.2 Feature Extraction
14.1.3 Script Modeling
14.1.4 Language Modeling and Search
14.2 Recognition System of RWTH Aachen University
14.2.1 Preprocessing
14.2.2 Feature Extraction
14.2.3 Script Modeling
14.2.4 Language Modeling and Search
14.3 ESMERALDA Offline Recognition System
14.3.1 Preprocessing
14.3.2 Feature Extraction
14.3.3 Handwriting Model
14.3.4 Language Modeling and Search
14.4 Bag-of-Features Hidden Markov Models
15 Analysis of Biological Sequences
15.1 HMMER
15.1.1 Model Structure
15.1.2 Parameter Estimation
15.1.3 Interoperability
15.2 SAM
15.3 ESMERALDA
15.3.1 Feature Extraction
15.3.2 Statistical Models of Proteins
References
Index
同类热销排行榜
- 目送/人生三书
-
21世纪的《背影》 + 感人至深的“生死笔记”+ 龙应台亲手摄影 + 跨三代共读的人生之书！
华人世界率性犀利的一枝笔，龙应台独家...
- 顾城的诗(金版)(精)/蓝星诗库
- 人类群星闪耀时(插图本)/译林名著精选
- 牛津高阶英汉双解词典(附光盘第8版)(精)
- 文化苦旅(新版)
- 摆渡人
- 解忧杂货店(精)
- 骆驼祥子
- 曾国藩(又笨又慢平天下)
- 查令十字街84号(珍藏版)(精)
推荐书目
-
孩子你慢慢来/人生三书 华人世界率性犀利的一枝笔，龙应台独家授权《孩子你慢慢来》20周年经典新版。她的《...
-
时间简史(插图版) 相对论、黑洞、弯曲空间……这些词给我们的感觉是艰深、晦涩、难以理解而且与我们的...
-
本质(精) 改革开放40年，恰如一部四部曲的年代大戏。技术突变、产品迭代、产业升级、资本对接...