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Part II: HTK in Depth
4 The Operating Environment
4.1 The Command Line
4.2 Script Files
4.3 Configuration Files
4.4 Standard Options
4.5 Error Reporting
4.6 Strings and Names
4.7 Memory Management
4.8 Input/Output via Pipes and Networks
4.9 Byte-swapping of HTK data files
4.10 Summary
5 Speech Input/Output
5.1 General Mechanism
5.2 Speech Signal Processing
5.3 Linear Prediction Analysis
5.4 Filterbank Analysis
5.5 Energy Measures
5.6 Delta and Acceleration Coefficients
5.7 Storage of Parameter Files
5.7.1 HTK Format Parameter Files
5.7.2 Esignal Format Parameter Files
5.8 Waveform File Formats
5.8.1 HTK File Format
5.8.2 Esignal File Format
5.8.3 TIMIT File Format
5.8.4 NIST File Format
5.8.5 SCRIBE File Format
5.8.6 SDES1 File Format
5.8.7 AIFF File Format
5.8.8 SUNAU8 File Format
5.8.9 OGI File Format
5.8.10 WAVE File Format
5.8.11 ALIEN and NOHEAD File Formats
5.9 Direct Audio Input/Output
5.10 Multiple Input Streams
5.11 Vector Quantisation
5.12 Viewing Speech with HLIST
5.13 Copying and Coding using HCOPY
5.14 Version 1.5 Compatibility
5.15 Summary
6 Transcriptions and Label Files
6.1 Label File Structure
6.2 Label File Formats
6.2.1 HTK Label Files
6.2.2 ESPS Label Files
6.2.3 TIMIT Label Files
6.2.4 SCRIBE Label Files
6.3 Master Label Files
6.3.1 General Principles of MLFs
6.3.2 Syntax and Semantics
6.3.3 MLF Search
6.3.4 MLF Examples
6.4 Editing Label Files
6.5 Summary
7 HMM Definition Files
7.1 The HMM Parameters
7.2 Basic HMM Definitions
7.3 Macro Definitions
7.4 HMM Sets
7.5 Tied-Mixture Systems
7.6 Discrete Probability HMMs
7.7 Tee Models
7.8 Binary Storage Format
7.9 The HMM Definition Language
8 HMM Parameter Estimation
8.1 Training Strategies
8.2 Initialisation using HINIT
8.3 Flat Starting with HCOMPV
8.4 Isolated Unit Re-Estimation using HREST
8.5 Embedded Training using HEREST
8.6 Single-Pass Retraining
8.7 Parameter Re-Estimation Formulae
8.7.1 Viterbi Training (HINIT)
8.7.2 Forward/Backward Probabilities
8.7.3 Single Model Reestimation(HREST)
8.7.4 Embedded Model Reestimation(HEREST)
9 HMM System Refinement
9.1 Using HHED
9.2 Constructing Context-Dependent Models
9.3 Parameter Tying and Item Lists
9.4 Data-Driven Clustering
9.5 Tree-Based Clustering
9.6 Mixture Incrementing
9.7 Miscellaneous Operations
10 Discrete and Tied-Mixture Models
10.1 Modelling Discrete Sequences
10.2 Using Discrete Models with Speech
10.3 Tied Mixture Systems
10.4 Parameter Smoothing
11 Networks, Dictionaries and Language Models
11.1 How Networks are Used
11.2 Word Networks and Standard Lattice Format
11.3 Building a Word Network with HPARSE
11.4 Bigram Language Models
11.5 Building a Word Network with HBUILD
11.6 Testing a Word Network using HSGEN
11.7 Constructing a Dictionary
11.8 Word Network Expansion
11.9 Other Kinds of Recognition System
12 Decoding
12.1 Decoder Operation
12.2 Decoder Organisation
12.3 Recognition using Test Databases
12.4 Evaluating Recognition Results
12.5 Generating Forced Alignments
12.6 Recognition using Direct Audio Input
12.7 N-Best Lists and Lattices
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HTKBook
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