An integration of hidden Markov model and neural network for phoneme recognition.

by Patrick Shu Pui Ko. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 77-78). / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Introduction to Speech Recognition --- p.1 / Chapter 1.2 --- Classifications and Constraints of Speech Recognition Systems --- p.1 / Chapter 1.2.1 --- Isolated Subword Unit Recognition --- p.1 / Chapter 1.2.2 --- Isolated Word Recognition --- p.2 / Chapter 1.2.3 --- Continuous Speech Recognition --- p.2 / Chapter 1.3 --- Objective of the Thesis --- p.3 / Chapter 1.3.1 --- What is the Problem --- p.3 / Chapter 1.3.2 --- How the Problem is Approached --- p.3 / Chapter 1.3.3 --- The Organization of this Thesis --- p.3 / Chapter 2. --- Literature Review --- p.5 / Chapter 2.1 --- Approaches to the Problem of Speech Recognition --- p.5 / Chapter 2.1.1 --- Template-Based Approaches --- p.6 / Chapter 2.1.2 --- Knowledge-Based Approaches --- p.9 / Chapter 2.1.3 --- Stochastic Approaches --- p.10 / Chapter 2.1.4 --- Connectionist Approaches --- p.14 / Chapter 3. --- Discrimination Issues of HMM --- p.16 / Chapter 3.1 --- Maximum Likelihood Estimation (MLE) --- p.16 / Chapter 3.2 --- Maximum Mutual Information (MMI) --- p.17 / Chapter 4. --- Neural Networks --- p.19 / Chapter 4.1 --- History --- p.19 / Chapter 4.2 --- Basic Concepts --- p.20 / Chapter 4.3 --- Learning --- p.21 / Chapter 4.3.1 --- Supervised Training --- p.21 / Chapter 4.3.2 --- Reinforcement Training --- p.22 / Chapter 4.3.3 --- Self-Organization --- p.22 / Chapter 4.4 --- Error Back-propagation --- p.22 / Chapter 5. --- Proposal of a Discriminative Neural Network Layer --- p.25 / Chapter 5.1 --- Rationale --- p.25 / Chapter 5.2 --- HMM Parameters --- p.27 / Chapter 5.3 --- Neural Network Layer --- p.28 / Chapter 5.4 --- Decision Rules --- p.29 / Chapter 6. --- Data Preparation --- p.31 / Chapter 6.1 --- TIMIT --- p.31 / Chapter 6.2 --- Feature Extraction --- p.34 / Chapter 6.3 --- Training --- p.43 / Chapter 7. --- Experiments and Results --- p.52 / Chapter 7.1 --- Experiments --- p.52 / Chapter 7.2 --- Experiment I --- p.52 / Chapter 7.3 --- Experiment II --- p.55 / Chapter 7.4 --- Experiment III --- p.57 / Chapter 7.5 --- Experiment IV --- p.58 / Chapter 7.6 --- Experiment V --- p.60 / Chapter 7.7 --- Computational Issues --- p.62 / Chapter 7.8 --- Limitations --- p.63 / Chapter 8. --- Conclusion --- p.64 / Chapter 9. --- Future Directions --- p.67 / Appendix / Chapter A. --- Linear Predictive Coding --- p.69 / Chapter B. --- Implementation of a Vector Quantizer --- p.70 / Chapter C. --- Implementation of HMM --- p.73 / Chapter C.1 --- Calculations Underflow --- p.73 / Chapter C.2 --- Zero-lising Effect --- p.75 / Chapter C.3 --- Training With Multiple Observation Sequences --- p.76 / References --- p.77

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_319161
Date January 1993
ContributorsKo, Patrick Shu Pui., Chinese University of Hong Kong Graduate School. Division of Computer Science.
PublisherChinese University of Hong Kong
Source SetsThe Chinese University of Hong Kong
LanguageEnglish
Detected LanguageEnglish
TypeText, bibliography
Formatprint, [ii], 78 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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