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