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.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- An Overview on Automatic Speech Recognition --- p.2 / Chapter 1.2 --- Thesis Outline --- p.6 / Chapter 2 --- Baseline Speech Recognition System --- p.8 / Chapter 2.1 --- Baseline Speech Recognition Framework --- p.8 / Chapter 2.2 --- Acoustic Feature Extraction --- p.11 / Chapter 2.2.1 --- Speech Production and Source-Filter Model --- p.12 / Chapter 2.2.2 --- Review of Feature Representations --- p.14 / Chapter 2.2.3 --- Mel-frequency Cepstral Coefficients --- p.20 / Chapter 2.2.4 --- Energy and Dynamic Features --- p.24 / Chapter 2.3 --- Back-end Decoder --- p.26 / Chapter 2.4 --- English Digit String Corpus ´ؤ AURORA2 --- p.28 / Chapter 2.5 --- Baseline Recognition Experiment --- p.31 / Chapter 3 --- A Simple Recognition Framework with Model Selection --- p.34 / Chapter 3.1 --- Mismatch between Training and Testing Conditions --- p.34 / Chapter 3.2 --- Matched Training and Testing Conditions --- p.38 / Chapter 3.2.1 --- Noise type-Matching --- p.38 / Chapter 3.2.2 --- SNR-Matching --- p.43 / Chapter 3.2.3 --- Noise Type and SNR-Matching --- p.44 / Chapter 3.3 --- Recognition Framework with Model Selection --- p.48 / Chapter 4 --- Noise Spectral Estimation --- p.53 / Chapter 4.1 --- Introduction to Statistical Estimation Methods --- p.53 / Chapter 4.1.1 --- Conventional Estimation Methods --- p.54 / Chapter 4.1.2 --- Histogram Technique --- p.55 / Chapter 4.2 --- Quantile-based Noise Estimation (QBNE) --- p.57 / Chapter 4.2.1 --- Overview of Quantile-based Noise Estimation (QBNE) --- p.58 / Chapter 4.2.2 --- Time-Frequency Quantile-based Noise Estimation (T-F QBNE) --- p.62 / Chapter 4.2.3 --- Mainlobe-Resilient Time-Frequency Quantile-based Noise Estimation (M-R T-F QBNE) --- p.65 / Chapter 4.3 --- Estimation Performance Analysis --- p.72 / Chapter 4.4 --- Recognition Experiment with Model Selection --- p.74 / Chapter 5 --- Feature Compensation: Algorithm and Experiment --- p.81 / Chapter 5.1 --- Feature Deviation from Clean Speech --- p.81 / Chapter 5.1.1 --- Deviation in MFCC Features --- p.82 / Chapter 5.1.2 --- Implications for Feature Compensation --- p.84 / Chapter 5.2 --- Overview of Conventional Compensation Methods --- p.86 / Chapter 5.3 --- Feature Compensation by In-phase Feature Induction --- p.94 / Chapter 5.3.1 --- Motivation --- p.94 / Chapter 5.3.2 --- Methodology --- p.97 / Chapter 5.4 --- Compensation Framework for Magnitude Spectrum and Segmen- tal Energy --- p.102 / Chapter 5.5 --- Recognition -Experiments --- p.103 / Chapter 6 --- Conclusions --- p.112 / Chapter 6.1 --- Summary and Discussions --- p.112 / Chapter 6.2 --- Future Directions --- p.114 / Bibliography --- p.116
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324930 |
| Date | January 2004 |
| Contributors | Lee, Siu Wa., Chinese University of Hong Kong Graduate School. Division of Electronic Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xv, 121 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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