by Francis Cho-yiu Chik. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 129-135). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Automatic speech recognition and its applications --- p.1 / Chapter 1.1.1 --- Artificial Neural Network (ANN) approach --- p.3 / Chapter 1.2 --- Motivation --- p.5 / Chapter 1.3 --- Background --- p.6 / Chapter 1.3.1 --- Speech recognition --- p.6 / Chapter 1.3.2 --- Parallel processing --- p.7 / Chapter 1.3.3 --- Parallel architectures --- p.10 / Chapter 1.3.4 --- Transputer --- p.12 / Chapter 1.4 --- Thesis outline --- p.13 / Chapter 2 --- Speech Signal Pre-processing --- p.14 / Chapter 2.1 --- Determine useful signal --- p.14 / Chapter 2.1.1 --- End point detection using energy --- p.15 / Chapter 2.1.2 --- End point detection enhancement using zero crossing rate --- p.18 / Chapter 2.2 --- Pre-emphasis filter --- p.19 / Chapter 2.3 --- Feature extraction --- p.20 / Chapter 2.3.1 --- Filter-bank spectrum analysis model --- p.22 / Chapter 2.3.2 --- Linear Predictive Coding (LPC) coefficients --- p.25 / Chapter 2.3.3 --- Cepstral coefficients --- p.27 / Chapter 2.3.4 --- Zero crossing rate and energy --- p.27 / Chapter 2.3.5 --- Pitch (fundamental frequency) detection --- p.28 / Chapter 2.4 --- Discussions --- p.30 / Chapter 3 --- Speech Recognition Methods --- p.32 / Chapter 3.1 --- Template matching using Dynamic Time Warping (DTW) --- p.32 / Chapter 3.2 --- Hidden Markov Model (HMM) --- p.37 / Chapter 3.2.1 --- Vector Quantization (VQ) --- p.38 / Chapter 3.2.2 --- Description of a discrete HMM --- p.41 / Chapter 3.2.3 --- Probability evaluation --- p.42 / Chapter 3.2.4 --- Estimation technique for model parameters --- p.46 / Chapter 3.2.5 --- State sequence for the observation sequence --- p.48 / Chapter 3.3 --- 2-dimensional Hidden Markov Model (2dHMM) --- p.49 / Chapter 3.3.1 --- Calculation for a 2dHMM --- p.50 / Chapter 3.4 --- Discussions --- p.56 / Chapter 4 --- Implementation --- p.59 / Chapter 4.1 --- Transputer based multiprocessor system --- p.59 / Chapter 4.1.1 --- Transputer Development System (TDS) --- p.60 / Chapter 4.1.2 --- System architecture --- p.61 / Chapter 4.1.3 --- Transtech TMB16 mother board --- p.62 / Chapter 4.1.4 --- Farming technique --- p.64 / Chapter 4.2 --- Farming technique on extracting spectral amplitude feature --- p.68 / Chapter 4.3 --- Feature extraction for LPC --- p.73 / Chapter 4.4 --- DTW based recognition --- p.77 / Chapter 4.4.1 --- Feature extraction --- p.77 / Chapter 4.4.2 --- Training and matching --- p.78 / Chapter 4.5 --- HMM based recognition --- p.80 / Chapter 4.5.1 --- Feature extraction --- p.80 / Chapter 4.5.2 --- Model training and matching --- p.81 / Chapter 4.6 --- 2dHMM based recognition --- p.83 / Chapter 4.6.1 --- Feature extraction --- p.83 / Chapter 4.6.2 --- Training --- p.83 / Chapter 4.6.3 --- Recognition --- p.87 / Chapter 4.7 --- Training convergence in HMM and 2dHMM --- p.88 / Chapter 4.8 --- Discussions --- p.91 / Chapter 5 --- Experimental Results --- p.92 / Chapter 5.1 --- "Comparison of DTW, HMM and 2dHMM" --- p.93 / Chapter 5.2 --- Comparison between HMM and 2dHMM --- p.98 / Chapter 5.2.1 --- Recognition test on 20 English words --- p.98 / Chapter 5.2.2 --- Recognition test on 10 Cantonese syllables --- p.102 / Chapter 5.3 --- Recognition test on 80 Cantonese syllables --- p.113 / Chapter 5.4 --- Speed matching --- p.118 / Chapter 5.5 --- Computational performance --- p.119 / Chapter 5.5.1 --- Training performance --- p.119 / Chapter 5.5.2 --- Recognition performance --- p.120 / Chapter 6 --- Discussions and Conclusions --- p.126 / Bibliography --- p.129 / Chapter A --- An ANN Model for Speech Recognition --- p.136 / Chapter B --- A Speech Signal Represented in Fequency Domain (Spectrogram) --- p.138 / Chapter C --- Dynamic Programming --- p.144 / Chapter D --- Markov Process --- p.145 / Chapter E --- Maximum Likelihood (ML) --- p.146 / Chapter F --- Multiple Training --- p.149 / Chapter F.1 --- HMM --- p.150 / Chapter F.2 --- 2dHMM --- p.150 / Chapter G --- IMS T800 Transputer --- p.152 / Chapter G.1 --- IMS T800 architecture --- p.152 / Chapter G.2 --- Instruction encoding --- p.153 / Chapter G.3 --- Floating point instructions --- p.155 / Chapter G.4 --- Optimizing use of the stack --- p.157 / Chapter G.5 --- Concurrent operation of FPU and CPU --- p.158
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_321551 |
| Date | January 1996 |
| Contributors | Chik, Francis Cho-yiu., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Publisher | Chinese University of Hong Kong |
| Source Sets | The Chinese University of Hong Kong |
| Language | English |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xiii, 158 leaves : ill. ; 30 cm. |
| Rights | Use 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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