Spelling suggestions: "subject:"[een] SPEECH RECOGNITION"" "subject:"[enn] SPEECH RECOGNITION""
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On the robustness of static and dynamic spectral information for speech recognition in noise. / CUHK electronic theses & dissertations collectionJanuary 2004 (has links)
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.
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Robust speaker recognition using both vocal source and vocal tract features estimated from noisy input utterances.January 2007 (has links)
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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Integrating computational auditory scene analysis and automatic speech recognitionSrinivasan, Soundararajan, January 2006 (has links)
Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 173-186).
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Speaker dynamics as a source of pronunciation variability for continuous speech recognition models /Bates, Rebecca Anne. January 2004 (has links)
Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 139-151).
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Kernel eigenvoice speaker adaptation /Ho, Ka-Lung. January 2003 (has links)
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.
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Strategies for improving audible quality and speech recognition accuracy of reverberant speech /Gillespie, Bradford W. January 2002 (has links)
Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 103-108).
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Long-term autocorrelation coefficients in automatic speech recognitionOdom, Warren Edward, 1952- January 1976 (has links)
No description available.
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A projection-based measure for automatic speech recognition in noiseCarlson, Beth A. 12 1900 (has links)
No description available.
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Distortion compensation in speech signals using a blind iterative algorithm based on memoryless symmetrical nonlinearitiesHubert-Brierre, Florent Maxime 08 1900 (has links)
No description available.
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Towards pose invariant visual speech processingPass, A. R. January 2013 (has links)
No description available.
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