Fundamentals of Speech Recognition
| Author | : | |
| Rating | : | 4.25 (523 Votes) |
| Asin | : | 0130151572 |
| Format Type | : | paperback |
| Number of Pages | : | 496 Pages |
| Publish Date | : | 2016-01-26 |
| Language | : | English |
DESCRIPTION:
Covers production, perception, and acoustic-phonetic characterization of the speech signal; signal processing and analysis methods for speech recognition; pattern comparison techniques; speech recognition system design and implementation; theory and implementation of hidden Markov models; speech recognition based on connected word models; large vocabulary continuous speech recognition; and task- oriented application of automatic speech recognition. For practicing engineers, scientists, linguists, and programmers interested in speech recognition. From the Back Cover Provides a theoretically sound, technically accurate, and complete description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine.
Five Stars Thomas Quintana Everything went very smooth.. "Excellent Introduction" according to Optimistix. This book is a comprehensive and excellent introduction to the ever-expanding field of Automatic Speech Recognition. Starting with models of speech production, speech characterization, methods of analysis (transforms etc), the authors go onto discuss pattern comparison, hidden Markov models (HMMs), and desi. A Customer said Hidden Markov Modeling primer. The book is focused on extraction of attributes of human speech with modern digital and mathematical tools. In Rabiner's approach, an essential element is the Hidden Markov Model and its ability to predict the state of a non-observable system. HM models are the topic of sophisticated techniques and study, a
Covers production, perception, and acoustic-phonetic characterization of the speech signal; signal processing and analysis methods for speech recognition; pattern comparison techniques; speech recognition system design and implementation; theory and implementation of hidden Markov models; speech recognition based on connected word models; large vocabulary continuous speech recognition; and task- oriented application of automatic speech recognition. Provides a theoretically sound, technically accurate, and complete description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine. For pr