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. Language: English