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Discrete-Time Processing of Speech Signals
Abstract
Commercial applications of speech processing and recognition are fast becoming a growth industry that will shape the next decade. Now students and practicing engineers of signal processing can find in a single volume the fundamentals essential to understanding this rapidly developing field. IEEE Press is pleased to publish a classic reissue of Discrete-Time Processing of Speech Signals. Specially featured in this reissue is the addition of valuable World Wide Web links to the latest speech data references. This landmark book offers a balanced discussion of both the mathematical theory of digital speech signal processing and critical contemporary applications. The authors provide a comprehensive view of all major modern speech processing areas: speech production physiology and modeling, signal analysis techniques, coding, enhancement, quality assessment, and recognition. You will learn the principles needed to understand advanced technologies in speech processing -- from speech coding for communications systems to biomedical applications of speech analysis and recognition. Ideal for self-study or as a course text, this far-reaching reference book offers an extensive historical context for concepts under discussion, end-of-chapter problems, and practical algorithms. Discrete-Time Processing of Speech Signals is the definitive resource for students, engineers, and scientists in the speech processing field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department
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Citations
Cited by
Wavelet-based statistical signal processing using hidden Markov models
Crouse, M. S., Nowak, R. D., & Baraniuk, R. G. (1998) · IEEE Transactions on Signal Processing · doi:10.1109/78.668544 · Cited by 1,643
Speech emotion recognition using hidden Markov models
Nwe, T. L., Foo, S. W., & De Silva, L. C. (2003) · Speech Communication · doi:10.1016/s0167-6393(03)00099-2 · Cited by 920
Nonlinear feature based classification of speech under stress.
Zhou, G., Hansen, J. H. L., & Kaiser, J. F. (2001) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.905995 · Cited by 409
Self-citationGodino-Llorente, J. I., Gomez-Vilda, P., & Blanco-Velasco, M. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.871883 · Cited by 322
Layered representations for learning and inferring office activity from multiple sensory channels
Oliver, N., Garg, A., & Horvitz, E. (2004) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.02.004 · Cited by 316
Metadata
- Authors
- Deller, J. R., Hansen, J. H. L., & Proakis, J. G.
- Venue
- Institute of Electrical and Electronics Engineers eBooks
- Publisher
- IEEE
- Published
- 1999
- OpenAlex
- W1493163583
- Cited by
- 2,458
- References
- 0
- FWCI
- 34.00
- Type
- book
- Language
- en
- Open access
- closed
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