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
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_325556 |
| Date | January 2006 |
| Contributors | Chan, Wai Nang., 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, vi, 82 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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