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Continuous speech phoneme recognition using neural networks and grammar correction.

by Wai-Tat Fu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 104-[109]). / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Problem of Speech Recognition --- p.1 / Chapter 1.2 --- Why continuous speech recognition? --- p.5 / Chapter 1.3 --- Current status of continuous speech recognition --- p.6 / Chapter 1.4 --- Research Goal --- p.10 / Chapter 1.5 --- Thesis outline --- p.10 / Chapter 2 --- Current Approaches to Continuous Speech Recognition --- p.12 / Chapter 2.1 --- BASIC STEPS FOR CONTINUOUS SPEECH RECOGNITION --- p.12 / Chapter 2.2 --- THE HIDDEN MARKOV MODEL APPROACH --- p.16 / Chapter 2.2.1 --- Introduction --- p.16 / Chapter 2.2.2 --- Segmentation and Pattern Matching --- p.18 / Chapter 2.2.3 --- Word Formation and Syntactic Processing --- p.22 / Chapter 2.2.4 --- Discussion --- p.23 / Chapter 2.3 --- NEURAL NETWORK APPROACH --- p.24 / Chapter 2.3.1 --- Introduction --- p.24 / Chapter 2.3.2 --- Segmentation and Pattern Matching --- p.25 / Chapter 2.3.3 --- Discussion --- p.27 / Chapter 2.4 --- MLP/HMM HYBRID APPROACH --- p.28 / Chapter 2.4.1 --- Introduction --- p.28 / Chapter 2.4.2 --- Architecture of Hybrid MLP/HMM Systems --- p.29 / Chapter 2.4.3 --- Discussions --- p.30 / Chapter 2.5 --- SYNTACTIC GRAMMAR --- p.30 / Chapter 2.5.1 --- Introduction --- p.30 / Chapter 2.5.2 --- Word formation and Syntactic Processing --- p.31 / Chapter 2.5.3 --- Discussion --- p.32 / Chapter 2.6 --- SUMMARY --- p.32 / Chapter 3 --- Neural Network As Pattern Classifier --- p.34 / Chapter 3.1 --- INTRODUCTION --- p.34 / Chapter 3.2 --- TRAINING ALGORITHMS AND TOPOLOGIES --- p.35 / Chapter 3.2.1 --- Multilayer Perceptrons --- p.35 / Chapter 3.2.2 --- Recurrent Neural Networks --- p.39 / Chapter 3.2.3 --- Self-organizing Maps --- p.41 / Chapter 3.2.4 --- Learning Vector Quantization --- p.43 / Chapter 3.3 --- EXPERIMENTS --- p.44 / Chapter 3.3.1 --- The Data Set --- p.44 / Chapter 3.3.2 --- Preprocessing of the Speech Data --- p.45 / Chapter 3.3.3 --- The Pattern Classifiers --- p.50 / Chapter 3.4 --- RESULTS AND DISCUSSIONS --- p.53 / Chapter 4 --- High Level Context Information --- p.56 / Chapter 4.1 --- INTRODUCTION --- p.56 / Chapter 4.2 --- HIDDEN MARKOV MODEL APPROACH --- p.57 / Chapter 4.3 --- THE DYNAMIC PROGRAMMING APPROACH --- p.59 / Chapter 4.4 --- THE SYNTACTIC GRAMMAR APPROACH --- p.60 / Chapter 5 --- Finite State Grammar Network --- p.62 / Chapter 5.1 --- INTRODUCTION --- p.62 / Chapter 5.2 --- THE GRAMMAR COMPILATION --- p.63 / Chapter 5.2.1 --- Introduction --- p.63 / Chapter 5.2.2 --- K-Tails Clustering Method --- p.66 / Chapter 5.2.3 --- Inference of finite state grammar --- p.67 / Chapter 5.2.4 --- Error Correcting Parsing --- p.69 / Chapter 5.3 --- EXPERIMENT --- p.71 / Chapter 5.4 --- RESULTS AND DISCUSSIONS --- p.73 / Chapter 6 --- The Integrated System --- p.81 / Chapter 6.1 --- INTRODUCTION --- p.81 / Chapter 6.2 --- POSTPROCESSING OF NEURAL NETWORK OUTPUT --- p.82 / Chapter 6.2.1 --- Activation Threshold --- p.82 / Chapter 6.2.2 --- Duration Threshold --- p.85 / Chapter 6.2.3 --- Merging of Phoneme boundaries --- p.88 / Chapter 6.3 --- THE ERROR CORRECTING PARSER --- p.90 / Chapter 6.4 --- RESULTS AND DISCUSSIONS --- p.96 / Chapter 7 --- Conclusions --- p.101 / Bibliography --- p.105

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_320547
Date January 1995
ContributorsFu, Wai-tat., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering.
PublisherChinese University of Hong Kong
Source SetsThe Chinese University of Hong Kong
LanguageEnglish
Detected LanguageEnglish
TypeText, bibliography
Formatprint, v, 104, [5] 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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