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

Thesis (Ph.D.)--Boston University. / The general problem investigated in this research was the relationship of set and the defense of repression to perceptual recognition. A. comparison was made of the perceptual recognition threshold scores and the slope scores af a High Repressor and a Low Repressor group on three series of pictures -- an Aggressive series, a Neutral with a Theme series, and a Neutral without a Theme series. Three hundred and eighty-six students were administered a sentence-completion test which was used as the defense measure. This test was devised specifically to assess the repression of aggression. On the basis of extreme scores on this test, twenty-four subjects were selected as a High Repressor group and twenty-four subjects as a Low Repressor group. A Medium Repressor group was selected from the scores tbat fell between the High Repressor and the Low Repressor groups. Each member of these groups was individually administered a perceptual recognition task [TRUNCATED]

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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.

Older adults are worse than young adults at perceiving emotions in others. However, it is unclear why these age-related differences in emotion perception exist. The studies presented in this thesis investigated the cognitive, emotional and motivational factors influencing age differences in emotion perception. Study 1 revealed no age differences in mood congruence effects: sad faces were rated as more sad when participants experienced negative mood. In contrast, Study 2 demonstrated that sad mood impaired recognition accuracy for sad faces. Together, findings suggested that different methods of assessing emotion perception engage the use of discrete processing strategies. These mood influences on emotion perception are similar in young and older adults. Studies 3 and 4 investigated age differences in emotion perception tasks which are more realistic and contextualised than still photographs of facial expressions. Older adults were worse than young at recognising emotions from silent dynamic displays; however, older adults outperformed young in a film task that displayed emotional information in multiple modalities (Study 3). Study 4 suggested that the provision of vocal information was particularly beneficial to older adults. Furthermore, vocabulary mediated the relationship between age and performance on the contextual film task. However, age-related deficits in decoding basic emotions were established in a separate multi-modal video-based task. In addition, age differences in the perception of neutral expressions were also examined. Neutral expressions were interpreted as displaying positive emotions by older adults. Using a dual-task paradigm, Study 5 suggested that working memory processes are involved in decoding emotions. However, age-related declines in working memory were not driving age effects in emotion perception. Neuropsychological, motivational and cognitive explanations for these results are evaluated. Implications of these findings for older adults' social functioning are discussed.

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

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This thesis presents a new and powerful approach to the development of a general purpose machine vision system. The approach is inspired from anatomical considerations of the primate's vision system. The geometrical arrangement of cones on a primate's retina can be described in terms of a hexagonal grid. The importance of the hexagonal grid is that it possesses special computational features that are pertinent to the vision process. The fundamental thrust of this thesis emanates from the observation that this hexagonal grid can be described in terms of the mathematical object known as a Euclidean ring. The Euclidean ring is employed to generate an algebra of linear transformations which are appropriate for the processing of multidimensional vision data. A parallel autonomous segmentation algorithm for multidimensional vision data is described. The algebra and segmentation algorithm are implemented on a network of transputers. The implementation is discussed in the context of the outline of a general purpose machine vision system's design.