by Cheng Yoik. / Thesis submitted in: October 1998. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 91-95). / Text in English; abstract also in Chinese. / by Cheng Yoik. / Chapter 1 --- Introduction --- p.13 / Chapter 1.1 --- What is Speaker Verification --- p.13 / Chapter 1.2 --- Review on Recent Speaker Verification Research --- p.15 / Chapter 1.2.1 --- Hidden Markov Modeling --- p.15 / Chapter 1.2.2 --- Cohort Normalization Scoring --- p.16 / Chapter 1.3 --- Objective of Thesis --- p.17 / Chapter 1.3.1 --- Text-prompted Speaker Verification System --- p.18 / Chapter 1.3.2 --- Fundamental Frequency (FO) Information --- p.18 / Chapter 1.3.3 --- Cohort Normalization on Cantonese --- p.19 / Chapter 1.4 --- Chapter Outline --- p.19 / Chapter 2 --- System Description --- p.21 / Chapter 2.1 --- System Overview --- p.21 / Chapter 2.2 --- Speech Signal Representations --- p.23 / Chapter 2.2.1 --- LPC Cesptral Coefficients --- p.24 / Chapter 2.2.2 --- Prosodic Features --- p.27 / Chapter 2.3 --- HMM Modeling Technique --- p.30 / Chapter 2.4 --- Speaker Classification --- p.34 / Chapter 2.4.1 --- Likelihood Scoring --- p.34 / Chapter 2.4.2 --- Verification Process --- p.36 / Chapter 2.4.2.1 --- General Approach --- p.36 / Chapter 2.4.2.2 --- Normalization Approach --- p.37 / Chapter 2.4.3 --- Cohort Sets --- p.39 / Chapter 2.5 --- Summary --- p.41 / Chapter 3 --- Experimental Setup --- p.42 / Chapter 3.1 --- Introduction --- p.42 / Chapter 3.2 --- Databases --- p.42 / Chapter 3.2.1 --- Cantonese Database --- p.43 / Chapter 3.2.2 --- YOHO Corpus --- p.45 / Chapter 3.3 --- Feature Analysis --- p.46 / Chapter 3.4 --- Speaker Models --- p.47 / Chapter 3.5 --- Experiments --- p.48 / Chapter 3.5.1 --- Evaluation --- p.48 / Chapter 3.5.2 --- FO Experiments --- p.50 / Chapter 3.5.2.1 --- FO Value --- p.51 / Chapter 3.5.2.2 --- Log FO Value --- p.51 / Chapter 3.5.2.3 --- Normalized FO --- p.52 / Chapter 3.5.2.4 --- Normalized Log FO --- p.53 / Chapter 3.5.3 --- Cohort Normalization Experiments --- p.53 / Chapter 3.5.3.1 --- Preliminary Study --- p.55 / Chapter 3.5.3.2 --- Cohort Normalization on Cantonese --- p.57 / Chapter 3.5.3.3 --- Cohort Normalization with Pitch Information on Cantonese --- p.58 / Chapter 4 --- Results and Analysis --- p.59 / Chapter 4.1 --- Introduction --- p.59 / Chapter 4.2 --- FO Experiments --- p.60 / Chapter 4.2.1 --- Results of Various Representation of FO Value on Cantonese --- p.60 / Chapter 4.2.2 --- Performance Comparison between Cantonese and English --- p.63 / Chapter 4.3 --- Cohort Normalization Experiments --- p.67 / Chapter 4.3.1 --- Performance Comparison on Our Results to Other Researches --- p.67 / Chapter 4.3.2 --- Results of Applying Cohort Normalization on Cantonese --- p.71 / Chapter 4.3.3 --- Results of Applying Cohort Normalization with Pitch Information on Cantonese --- p.74 / Chapter 4.4 --- Summary --- p.79 / Chapter 5 --- Conclusions and Future Work --- p.81 / Chapter 5.1 --- Conclusions --- p.81 / Chapter 5.2 --- Future Work --- p.82 / Chapter 5.2.1 --- Refinements --- p.82 / Chapter 5.2.2 --- Formant --- p.84 / Chapter 5.2.3 --- Independent Cohort Models --- p.85 / Chapter 6 --- Application --- p.86 / Chapter 6.1 --- Overview --- p.86 / Chapter 6.2 --- Telephony Interface --- p.87 / Chapter 6.3 --- Verification --- p.88 / Chapter 6.4 --- Discussion --- p.89

Chan Wai Nang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 77-82). / Abstracts in English and Chinese. / Chapter Chapter1 --- Introduction --- p.1 / Chapter 1.1 --- Speaker recognition --- p.1 / Chapter 1.2 --- State of the art of speaker recognition techniques --- p.2 / Chapter 1.3 --- Motivations --- p.5 / Chapter 1.4 --- Thesis outline --- p.6 / Chapter Chapter2 --- Acoustic Features --- p.8 / Chapter 2.1 --- Speech production --- p.8 / Chapter 2.1.1 --- Physiology of speech production --- p.8 / Chapter 2.1.2 --- Source-filter model --- p.11 / Chapter 2.2 --- Vocal tract and vocal source related acoustic features --- p.14 / Chapter 2.3 --- Linear predictive analysis of speech --- p.15 / Chapter 2.4 --- Features for speaker recognition --- p.16 / Chapter 2.4.1 --- Vocal tract related features --- p.17 / Chapter 2.4.2 --- Vocal source related features --- p.19 / Chapter 2.5 --- Wavelet octave coefficients of residues (WOCOR) --- p.20 / Chapter Chapter3 --- Statistical approaches to speaker recognition --- p.24 / Chapter 3.1 --- Statistical modeling --- p.24 / Chapter 3.1.1 --- Classification and modeling --- p.24 / Chapter 3.1.2 --- Parametric vs non-parametric --- p.25 / Chapter 3.1.3 --- Gaussian mixture model (GMM) --- p.25 / Chapter 3.1.4 --- Model estimation --- p.27 / Chapter 3.2 --- Classification --- p.28 / Chapter 3.2.1 --- Multi-class classification for speaker identification --- p.28 / Chapter 3.2.2 --- Two-speaker recognition --- p.29 / Chapter 3.2.3 --- Model selection by statistical model --- p.30 / Chapter 3.2.4 --- Performance evaluation metric --- p.31 / Chapter Chapter4 --- Content dependency study of WOCOR and MFCC --- p.32 / Chapter 4.1 --- Database: CU2C --- p.32 / Chapter 4.2 --- Methods and procedures --- p.33 / Chapter 4.3 --- Experimental results --- p.35 / Chapter 4.4 --- Discussion --- p.36 / Chapter 4.5 --- Detailed analysis --- p.39 / Summary --- p.41 / Chapter Chapter5 --- Speaker Segmentation --- p.43 / Chapter 5.1 --- Feature extraction --- p.43 / Chapter 5.2 --- Statistical methods for segmentation and clustering --- p.44 / Chapter 5.2.1 --- Segmentation by spectral difference --- p.44 / Chapter 5.2.2 --- Segmentation by Bayesian information criterion (BIC) --- p.47 / Chapter 5.2.3 --- Segment clustering by BIC --- p.49 / Chapter 5.3 --- Baseline system --- p.50 / Chapter 5.3.1 --- Algorithm --- p.50 / Chapter 5.3.2 --- Speech database --- p.52 / Chapter 5.3.3 --- Performance metric --- p.53 / Chapter 5.3.4 --- Results --- p.58 / Summary --- p.60 / Chapter Chapter6 --- Application of vocal source features in speaker segmentation --- p.61 / Chapter 6.1 --- Discrimination power of WOCOR against MFCC --- p.61 / Chapter 6.1.1 --- Experimental set-up --- p.62 / Chapter 6.1.2 --- Results --- p.63 / Chapter 6.2 --- Speaker segmentation using vocal source features --- p.67 / Chapter 6.2.1 --- The construction of new proposed system --- p.67 / Summary --- p.72 / Chapter Chapter7 --- Conclusions --- p.74 / Reference --- p.77

No description available.

Tam Yuk Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1. --- Biometrics for User Authentication --- p.2 / Chapter 1.2. --- Voice-based User Authentication --- p.6 / Chapter 1.3. --- Motivation and Focus of This Work --- p.7 / Chapter 1.4. --- Thesis Outline --- p.9 / References --- p.11 / Chapter Chapter 2 --- Fundamentals of Automatic Speaker Recognition --- p.14 / Chapter 2.1. --- Speech Production --- p.14 / Chapter 2.2. --- Features of Speaker's Voice in Speech Signal --- p.16 / Chapter 2.3. --- Basics of Speaker Recognition --- p.19 / Chapter 2.4. --- Existing Approaches of Speaker Recognition --- p.20 / Chapter 2.4.1. --- Feature Extraction --- p.21 / Chapter 2.4.1.1 --- Overview --- p.21 / Chapter 2.4.1.2 --- Mel-Frequency Cepstral Coefficient (MFCC) --- p.21 / Chapter 2.4.2. --- Speaker Modeling --- p.24 / Chapter 2.4.2.1 --- Overview --- p.24 / Chapter 2.4.2.2 --- Gaussian Mixture Model (GMM) --- p.25 / Chapter 2.4.3. --- Speaker Identification (SID) --- p.26 / References --- p.29 / Chapter Chapter 3 --- Data Collection and Baseline System --- p.32 / Chapter 3.1. --- Data Collection --- p.32 / Chapter 3.2. --- Baseline System --- p.36 / Chapter 3.2.1. --- Experimental Set-up --- p.36 / Chapter 3.2.2. --- Results and Analysis --- p.39 / References --- p.42 / Chapter Chapter 4 --- Subband Spectral Envelope Features --- p.44 / Chapter 4.1. --- Spectral Envelope Features --- p.44 / Chapter 4.2. --- Subband Spectral Envelope Features --- p.46 / Chapter 4.3. --- Feature Extraction Procedures --- p.52 / Chapter 4.4. --- SID Experiments --- p.55 / Chapter 4.4.1. --- Experimental Set-up --- p.55 / Chapter 4.4.2. --- Results and Analysis --- p.55 / References --- p.62 / Chapter Chapter 5 --- Fusion of Subband Features --- p.63 / Chapter 5.1. --- Model Level Fusion --- p.63 / Chapter 5.1.1. --- Experimental Set-up --- p.63 / Chapter 5.1.2. --- "Results and Analysis," --- p.65 / Chapter 5.2. --- Feature Level Fusion --- p.69 / Chapter 5.2.1. --- Experimental Set-up --- p.70 / Chapter 5.2.2. --- "Results and Analysis," --- p.71 / Chapter 5.3. --- Discussion --- p.73 / References --- p.75 / Chapter Chapter 6 --- Utterance-Level SID with Text-Dependent Weights --- p.77 / Chapter 6.1. --- Motivation --- p.77 / Chapter 6.2. --- Utterance-Level SID --- p.78 / Chapter 6.3. --- Baseline System --- p.79 / Chapter 6.3.1. --- Implementation Details --- p.79 / Chapter 6.3.2. --- "Results and Analysis," --- p.80 / Chapter 6.4. --- Text-Dependent Weights --- p.81 / Chapter 6.4.1. --- Implementation Details --- p.81 / Chapter 6.4.2. --- "Results and Analysis," --- p.83 / Chapter 6.5. --- Text-Dependent Feature Weights --- p.86 / Chapter 6.5.1. --- Implementation Details --- p.86 / Chapter 6.5.2. --- "Results and Analysis," --- p.87 / Chapter 6.6. --- Text-Dependent Weights Applied in Score Combination and Subband Features --- p.88 / Chapter 6.6.1. --- Implementation Details --- p.89 / Chapter 6.6.2. --- Results and Analysis --- p.89 / Chapter 6.7. --- Discussion --- p.90 / Chapter Chapter 7 --- Conclusions and Suggested Future Work --- p.92 / Chapter 7.1. --- Conclusions --- p.92 / Chapter 7.2. --- Suggested Future Work --- p.94 / Appendix --- p.96 / Appendix 1 Speech Content for Data Collection --- p.96

Yang Chen. / "November 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 131-141) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Wang, Ning. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 106-115). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction to Speech and Speaker Recognition --- p.1 / Chapter 1.2 --- Difficulties and Challenges of Speaker Authentication --- p.6 / Chapter 1.3 --- Objectives and Thesis Outline --- p.7 / Chapter 2 --- Speaker Recognition System --- p.10 / Chapter 2.1 --- Baseline Speaker Recognition System Overview --- p.10 / Chapter 2.1.1 --- Feature Extraction --- p.12 / Chapter 2.1.2 --- Pattern Generation and Classification --- p.24 / Chapter 2.2 --- Performance Evaluation Metric for Different Speaker Recognition Tasks --- p.30 / Chapter 2.3 --- Robustness of Speaker Recognition System --- p.30 / Chapter 2.3.1 --- Speech Corpus: CU2C --- p.30 / Chapter 2.3.2 --- Noise Database: NOISEX-92 --- p.34 / Chapter 2.3.3 --- Mismatched Training and Testing Conditions --- p.35 / Chapter 2.4 --- Summary --- p.37 / Chapter 3 --- Speaker Recognition System using both Vocal Tract and Vocal Source Features --- p.38 / Chapter 3.1 --- Speech Production Mechanism --- p.39 / Chapter 3.1.1 --- Speech Production: An Overview --- p.39 / Chapter 3.1.2 --- Acoustic Properties of Human Speech --- p.40 / Chapter 3.2 --- Source-filter Model and Linear Predictive Analysis --- p.44 / Chapter 3.2.1 --- Source-filter Speech Model --- p.44 / Chapter 3.2.2 --- Linear Predictive Analysis for Speech Signal --- p.46 / Chapter 3.3 --- Vocal Tract Features --- p.51 / Chapter 3.4 --- Vocal Source Features --- p.52 / Chapter 3.4.1 --- Source Related Features: An Overview --- p.52 / Chapter 3.4.2 --- Source Related Features: Technical Viewpoints --- p.54 / Chapter 3.5 --- Effects of Noises on Speech Properties --- p.55 / Chapter 3.6 --- Summary --- p.61 / Chapter 4 --- Estimation of Robust Acoustic Features for Speaker Discrimination --- p.62 / Chapter 4.1 --- Robust Speech Techniques --- p.63 / Chapter 4.1.1 --- Noise Resilience --- p.64 / Chapter 4.1.2 --- Speech Enhancement --- p.64 / Chapter 4.2 --- Spectral Subtractive-Type Preprocessing --- p.65 / Chapter 4.2.1 --- Noise Estimation --- p.66 / Chapter 4.2.2 --- Spectral Subtraction Algorithm --- p.66 / Chapter 4.3 --- LP Analysis of Noisy Speech --- p.67 / Chapter 4.3.1 --- LP Inverse Filtering: Whitening Process --- p.68 / Chapter 4.3.2 --- Magnitude Response of All-pole Filter in Noisy Condition --- p.70 / Chapter 4.3.3 --- Noise Spectral Reshaping --- p.72 / Chapter 4.4 --- Distinctive Vocal Tract and Vocal Source Feature Extraction . . --- p.73 / Chapter 4.4.1 --- Vocal Tract Feature Extraction --- p.73 / Chapter 4.4.2 --- Source Feature Generation Procedure --- p.75 / Chapter 4.4.3 --- Subband-specific Parameterization Method --- p.79 / Chapter 4.5 --- Summary --- p.87 / Chapter 5 --- Speaker Recognition Tasks & Performance Evaluation --- p.88 / Chapter 5.1 --- Speaker Recognition Experimental Setup --- p.89 / Chapter 5.1.1 --- Task Description --- p.89 / Chapter 5.1.2 --- Baseline Experiments --- p.90 / Chapter 5.1.3 --- Identification and Verification Results --- p.91 / Chapter 5.2 --- Speaker Recognition using Source-tract Features --- p.92 / Chapter 5.2.1 --- Source Feature Selection --- p.92 / Chapter 5.2.2 --- Source-tract Feature Fusion --- p.94 / Chapter 5.2.3 --- Identification and Verification Results --- p.95 / Chapter 5.3 --- Performance Analysis --- p.98 / Chapter 6 --- Conclusion --- p.102 / Chapter 6.1 --- Discussion and Conclusion --- p.102 / Chapter 6.2 --- Suggestion of Future Work --- p.104

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 173-186).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 139-151).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 56-61). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 103-108).