Robust speech recognition under noisy environments.

Lee Siu Wa.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 116-121).Abstracts in English and Chinese.Abstract --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- An Overview on Automatic Speech Recognition --- p.2Chapter 1.2 --- Thesis Outline --- p.6Chapter 2 --- Baseline Speech Recognition System --- p.8Chapter 2.1 --- Baseline Speech Recognition Framework --- p.8Chapter 2.2 --- Acoustic Feature Extraction --- p.11Chapter 2.2.1 --- Speech Production and Source-Filter Model --- p.12Chapter 2.2.2 --- Review of Feature Representations --- p.14Chapter 2.2.3 --- Mel-frequency Cepstral Coefficients --- p.20Chapter 2.2.4 --- Energy and Dynamic Features --- p.24Chapter 2.3 --- Back-end Decoder --- p.26Chapter 2.4 --- English Digit String Corpus ´ؤ AURORA2 --- p.28Chapter 2.5 --- Baseline Recognition Experiment --- p.31Chapter 3 --- A Simple Recognition Framework with Model Selection --- p.34Chapter 3.1 --- Mismatch between Training and Testing Conditions --- p.34Chapter 3.2 --- Matched Training and Testing Conditions --- p.38Chapter 3.2.1 --- Noise type-Matching --- p.38Chapter 3.2.2 --- SNR-Matching --- p.43Chapter 3.2.3 --- Noise Type and SNR-Matching --- p.44Chapter 3.3 --- Recognition Framework with Model Selection --- p.48Chapter 4 --- Noise Spectral Estimation --- p.53Chapter 4.1 --- Introduction to Statistical Estimation Methods --- p.53Chapter 4.1.1 --- Conventional Estimation Methods --- p.54Chapter 4.1.2 --- Histogram Technique --- p.55Chapter 4.2 --- Quantile-based Noise Estimation (QBNE) --- p.57Chapter 4.2.1 --- Overview of Quantile-based Noise Estimation (QBNE) --- p.58Chapter 4.2.2 --- Time-Frequency Quantile-based Noise Estimation (T-F QBNE) --- p.62Chapter 4.2.3 --- Mainlobe-Resilient Time-Frequency Quantile-based Noise Estimation (M-R T-F QBNE) --- p.65Chapter 4.3 --- Estimation Performance Analysis --- p.72Chapter 4.4 --- Recognition Experiment with Model Selection --- p.74Chapter 5 --- Feature Compensation: Algorithm and Experiment --- p.81Chapter 5.1 --- Feature Deviation from Clean Speech --- p.81Chapter 5.1.1 --- Deviation in MFCC Features --- p.82Chapter 5.1.2 --- Implications for Feature Compensation --- p.84Chapter 5.2 --- Overview of Conventional Compensation Methods --- p.86Chapter 5.3 --- Feature Compensation by In-phase Feature Induction --- p.94Chapter 5.3.1 --- Motivation --- p.94Chapter 5.3.2 --- Methodology --- p.97Chapter 5.4 --- Compensation Framework for Magnitude Spectrum and Segmen- tal Energy --- p.102Chapter 5.5 --- Recognition -Experiments --- p.103Chapter 6 --- Conclusions --- p.112Chapter 6.1 --- Summary and Discussions --- p.112Chapter 6.2 --- Future Directions --- p.114Bibliography --- p.11

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