Use of vocal source features in speaker segmentation.

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.1Chapter 1.1 --- Speaker recognition --- p.1Chapter 1.2 --- State of the art of speaker recognition techniques --- p.2Chapter 1.3 --- Motivations --- p.5Chapter 1.4 --- Thesis outline --- p.6Chapter Chapter2 --- Acoustic Features --- p.8Chapter 2.1 --- Speech production --- p.8Chapter 2.1.1 --- Physiology of speech production --- p.8Chapter 2.1.2 --- Source-filter model --- p.11Chapter 2.2 --- Vocal tract and vocal source related acoustic features --- p.14Chapter 2.3 --- Linear predictive analysis of speech --- p.15Chapter 2.4 --- Features for speaker recognition --- p.16Chapter 2.4.1 --- Vocal tract related features --- p.17Chapter 2.4.2 --- Vocal source related features --- p.19Chapter 2.5 --- Wavelet octave coefficients of residues (WOCOR) --- p.20Chapter Chapter3 --- Statistical approaches to speaker recognition --- p.24Chapter 3.1 --- Statistical modeling --- p.24Chapter 3.1.1 --- Classification and modeling --- p.24Chapter 3.1.2 --- Parametric vs non-parametric --- p.25Chapter 3.1.3 --- Gaussian mixture model (GMM) --- p.25Chapter 3.1.4 --- Model estimation --- p.27Chapter 3.2 --- Classification --- p.28Chapter 3.2.1 --- Multi-class classification for speaker identification --- p.28Chapter 3.2.2 --- Two-speaker recognition --- p.29Chapter 3.2.3 --- Model selection by statistical model --- p.30Chapter 3.2.4 --- Performance evaluation metric --- p.31Chapter Chapter4 --- Content dependency study of WOCOR and MFCC --- p.32Chapter 4.1 --- Database: CU2C --- p.32Chapter 4.2 --- Methods and procedures --- p.33Chapter 4.3 --- Experimental results --- p.35Chapter 4.4 --- Discussion --- p.36Chapter 4.5 --- Detailed analysis --- p.39Summary --- p.41Chapter Chapter5 --- Speaker Segmentation --- p.43Chapter 5.1 --- Feature extraction --- p.43Chapter 5.2 --- Statistical methods for segmentation and clustering --- p.44Chapter 5.2.1 --- Segmentation by spectral difference --- p.44Chapter 5.2.2 --- Segmentation by Bayesian information criterion (BIC) --- p.47Chapter 5.2.3 --- Segment clustering by BIC --- p.49Chapter 5.3 --- Baseline system --- p.50Chapter 5.3.1 --- Algorithm --- p.50Chapter 5.3.2 --- Speech database --- p.52Chapter 5.3.3 --- Performance metric --- p.53Chapter 5.3.4 --- Results --- p.58Summary --- p.60Chapter Chapter6 --- Application of vocal source features in speaker segmentation --- p.61Chapter 6.1 --- Discrimination power of WOCOR against MFCC --- p.61Chapter 6.1.1 --- Experimental set-up --- p.62Chapter 6.1.2 --- Results --- p.63Chapter 6.2 --- Speaker segmentation using vocal source features --- p.67Chapter 6.2.1 --- The construction of new proposed system --- p.67Summary --- p.72Chapter Chapter7 --- Conclusions --- p.74Reference --- p.7