Contents 1 Spoken Language Input 1 Ron Cole & Victor Zue, chapter editors 1.1 Overview : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 Victor Zue & Ron Cole 1.2 Speech Recognition : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 Victor Zue, Ron Cole, & Wayne Ward 1.3 Signal Representation : : : : : : : : : : : : : : : : : : : : : : : : : : 11 Melvyn J. Hunt 1.4 Robust Speech Recognition : : : : : : : : : : : : : : : : : : : : : : 17 Richard M. Stern 1.5 HMM Methods in Speech Recognition : : : : : : : : : : : : : : : 24 Renato De Mori & Fabio Brugnara 1.6 Language Representation : : : : : : : : : : : : : : : : : : : : : : : : 35 Salim Roukos 1.7 Speaker Recognition : : : : : : : : : : : : : : : : : : : : : : : : : : :<F35.37

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . 8 1.1. The Cross-Condition Problem . . . . . . . . . . . . . . . . . . . . 8 1.2. Thesis Statement . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3. Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2. Background . . . . . . . . . . . . . . . . . . . . .12 2.1. Delay-and-Sum Beamforming . . . . . . . . . . . . . . . . . . . 12 2.1.1. Application of Delay-and-Sum Processing to Speech Recognition . . 13 2.2. Traditional Adaptive Arrays . . . . . . . . . . . . . . . . . . . . 13 2.2.1. Adaptive Noise Cancelling . . . . . . . . . . . . . . . . . . 15 2.2.2. Application of Traditional Adaptive Methods to Speech Recognition . 16 2.3. Cross-Correlation Based Arrays . . . . . . . . . . . . . . . . . . 18 2.3.1. Phenomena . . . . . . . . ....

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1. Approaches to Overcoming Environmental Variability . . . . . . . . . . . . . . 6 1.1.1. Re-Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.2. Multi-Style Training . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.1.3. Environmental Compensation Using Dynamic Adaptation . . . . . . . . . . 8 1.2. Towards Environment-Independent Recognition . . . . . . . . . . . . . . . . 8 1.2.1. Sources of Environmental Variability . . . . . . . . . . . . . . . . . . 9 1.2.2. Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 9 1.3. Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 Overview of Environmental Robustness in Speech Recognition . . . . . . 12 2.1. Sources of Degradation...

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In this paper, a new auditory-based speech processing system based on the biologically rooted property of average localized synchrony detection (ALSD) is proposed. The system detects periodicity in the speech signal at Bark-scaled frequencies while reducing the response's spurious peaks and sensitivity to implementation mismatches, and hence presents a consistent and robust representation of the formants. The system is evaluated for its formant extraction ability while reducing spurious peaks. It is compared with other auditory-based front-end processing systems in the task of vowel recognition on clean speech from the TIMIT database and in the presence of noise. The results illustrate the advantage of the ALSD system in extracting the formants and reducing the spurious peaks. They also indicate the superiority of the synchrony measures over the mean-rate in the presence of noise. 1. INTRODUCTION Due to the superb ability of humans to recognize speech in noisy environments, auditory-...

Auditory-based speech processing based on the average localized synchrony detection

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Auditory-based speech processing based on the average localized synchrony detection