Automatic Speech Recognition: Fundamentals of Speech Recognition

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Fundamentals of Speech Recognition:

A Tutorial Based on a Public Domain C++ Toolkit
Table of Contents
Introduction

1. Installation 1.1 Overview 1.2 Using CVS Remotely 1.3 Using CVS Locally 1.4 Tar Distributions 1.5 Compilation 1.6 Testing 1.7 Setting up Tutorial Examples 1.8 Command Synopsis	4. Recognition 4.1 Overview 4.2 Network Decoding 4.3 Scoring 4.4 Forced Alignment 4.5 N-Best Generation 4.6 Word Graph Generation 4.7 Word Graph Rescoring 4.8 Word Graph Error Rate 4.9 Command Synopsis	7. Comprehensive Examples 7.1 Overview 7.2 TIDigits 7.3 Alphadigits 7.4 Resource Management 7.5 Wall Street Journal 7.6 Switchboard 7.7 Command Synopsis
2. Data Preparation 2.1 Overview 2.2 File Conversion 2.3 Downsampling 2.4 Auxiliary Resources 2.5 Command Synopsis	5. Acoustic Modeling 5.1 Overview 5.2 Word Models 5.3 CI Phone Models 5.4 Word Internal CD Models 5.5 Cross-Word CD Models 5.6 Parallel Training 5.7 Command Synopsis	Appendix A. Lecture Notes B. AJR: Speech Analysis C. CMU: Hephaestus D. HTK: Hidden Markov Model Toolkit E. NIST: Common Evaluations F. OGI: HLT Survey G. SDK: Speech Development Kit
3. Feature Extraction 3.1 Overview 3.2 Signal Flow Graphs 3.3 Rapid Prototyping 3.4 Transforming Input Signals 3.5 MFCC Example 3.6 Components 3.7 Command Synopsis	6. Language Modeling 6.1 Overview 6.2 Language Modeling Tools 6.3 N-Gram Modeling	Glossary A B C D E F G H I J K L M N O P Q R S T U V W X Y Z