Overview

""Acoustical and Environmental Robustness in Automatic Speech Recognition"" provides a comprehensive review and comparison of the major single-channel compensation strategies currently in the literature. It develops a unified cepstral representation that facilitates joint compensation for the effects of noise, filtering and frequency warping. Finally, it describes and explains the compensation algorithms that have been developed to compensate for these types of environmental degradation, and it provides the details needed to implement the algorithms. As such, the book serves as a reference as well as a text book for an advanced course on the subject.

Full Product Details

Author:   A. Acero
Publisher:   Springer
Imprint:   Springer
Edition:   1993 ed.
Volume:   201
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   1.050kg
ISBN:  

9780792392842

ISBN 10:   0792392841
Pages:   186
Publication Date:   30 November 1992
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

List of Figures.- List of Tables.- Foreword.- Acknowledgments.- 1. Introduction.- 1.1. Acoustical Environmental Variability and its Consequences.- 1.2. Previous Research in Signal Processing for Robust Speech Recognition.- 1.3. Towards Environment-Independent Recognition.- 1.4. Monograph Outline.- 2. Experimental Procedure.- 2.1. An Overview of SPHINX.- 2.2. The Census Database.- 2.3. Objective Measurements.- 2.4. Baseline Recognition Accuracy.- 2.5. Other Databases.- 2.6. Summary.- 3. Frequency Domain Processing.- 3.1. Multi-Style Training.- 3.2. Channel Equalization.- 3.3. Noise Suppression by Spectral Subtraction.- 3.4. Experiments with Sphinx.- 3.5. Summary.- 4. The SDCN Algorithm.- 4.1. A Model of the Environment.- 4.2. Processing in the Frequency Domain: The MMSEN Algorithm.- 4.3. Processing in the Cepstral Domain: The SDCN Algorithm.- 4.4. Summary.- 5. The CDCN Algorithm.- 5.1. Introduction to the CDCN Algorithm.- 5.2. MMSE Estimator of the Cepstral Vector.- 5.3. ML Estimation of Noise and Spectral Tilt.- 5.4. Implementation Details.- 5.5. Summary of the CDCN Algorithm.- 5.6. Evaluation Results.- 5.7. Summary.- 6. Other Algorithms.- 6.1. The ISDCN Algorithm.- 6.2. The BSDCN Algorithm.- 6.3. The FCDCN Algorithm.- 6.4. Environmental Adaptation in Real Time.- 6.5. Summary.- 7. Frequency Normalization.- 7.1. The Use of Mel-scale Parameters.- 7.2. Improved Frequency Resolution.- 7.3. Variable Frequency Warping.- 7.4. Summary.- 8. Summary of Results.- 9. Conclusions.- 9.1. Contributions.- 9.2. Suggestions for Future Work.- Appendix I. Glossary.- Appendix II. Signal Processing in Sphinx.- Appendix III. The Bilinear Transform.- Appendix IV. Spectral Estimation Issues.- Appendix V. MMSE Estimation in the CDCN Algorithm.- Appendix VI. Maximum Likelihood via the EM Algorithm.- Appendix VII. ML Estimation of Noise and Spectral Tilt.- Appendix VIII. Vocabulary and Pronunciation Dictionary.- References.

Reviews

Author Information

Tab Content 6

Author Website:  

Customer Reviews

Recent Reviews

No review item found!

Add your own review!

Countries Available

All regions