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In this book we address robustness issues at the speech recognition and natural language parsing levels, with a focus on feature extraction and noise robust recognition, adaptive systems, language modeling, parsing, and natural language understanding. This book attempts to give a clear overview of the main technologies used in language and speech processing, along with an extensive bibliography to enable topics of interest to be pursued further. It also brings together speech and language technologies often considered separately.
Robustness in Language and Speech Technology serves as a valuable reference and although not intended as a formal university textbook, contains some material that can be used for a course at the graduate or undergraduate level.

Robustness in Language and Speech Technology

1. Introduction; J.-C. Junqua, G. van Noord. 2. Acoustic Features and Distance Measure; J. de Veth, et al. 3. Speaker Compensation in Automatic Speech Recognition; D.T. Merino. 4. Robustness in Statistical Language Modeling; J.R. Bellegarda. 5. Improving Robustness by Modeling Spontaneous Speech Events; P.A. Heeman, J.F. Allen. 6. Regular Approximation of Context-Free Grammars; M. Mohri, M.-J. Nederhof. 7. Weighted Grammer Tools: The GRM Library; M. Mohri. 8. Robust Parsing and Beyond; J.-P. Chanod. 9. Robust Parsing of Word Graphs; G. van Noord. 10. Balancing Robustness and Efficiency; C. Penstein Rosé, A. Lavie.