Multi-transputer based isolated word speech recognition system.

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