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Rating: 
Summary: Autometic Connectedword Large vocubalury SpeechRecognition
Review: Ifound this book is very helpfull for Speech Recognition and any other Speech Processing
Rating: 
Summary: Good but contaminated with Linear Predictive Coding
Review: Since this book misguides students of speech signal processing with the outdated compression technique of Linear Predictive Coding (LPC, which is far inferior to cepstral vocoding because of LPC's stateful memory of voiced excitation from one frame to the next), it ought to be half the price of Jelinek's book, not twice.
Rating: 
Summary: Good introduction for beginners
Review: The beginner in Automaitc Speech Recognition should read this book. It introduces all the basics of signal processing and vocal tract modeling needed and provides good descriptions of modern algorithms for statistical speech recognition (such as dynamic programmation, Hidden Markov Models, Viterbi Algorithm ...).
Rating:
Summary: Hidden Markov Modeling primer
Review: 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, and so independent fundamental description of the methods are infrequent. Rabiner goes out of his way to clearly define the HMM before addressing its specific application to speech processing. A very useful introduction to the HMM.
Rating:
Summary: Excellent Introduction
Review: 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 design and implementation of speech recognition systems, right from
isolated word recognition to large vocabulary continuous speech recognition
systems. Neural networks and their use in speech recognition is also presented,
though somewhat briefly. Rabiner was the author of the first widely-read tutorial on HMMs, so
naturally the presentation of HMMs is one of the strong points of this
textbook. The theory is developed in detail, but in an easy to follow
fashion, starting with the very basics and with plenty of helpful examples.
The implementation is discussed at great length as well, starting with
the simplest of tasks and progressing to the state-of-the-art (circa 1993). That isn't to say that HMMs are the only good part of this book - indeed,
practically every topic, whether it be perception, transforms, vector quantization
or dynamic programming, is presented with great clarity. This book really is easy to
learn from, with numerous examples and illustrations. The field of speech recognition is inherently multi-disciplinary in nature,
drawing upon various areas of study, including Physics, Physiology, Acoustics,
Signal Processing and Computer Science, to name but a few. The authors do a
great job of explaining all these facets, as well as the mathematics that
is an essential tool.
The only caveat is that it's now a little old (published 1993), since the
field has been growing by leaps and bounds - so while the basics remain
the same, things have changed and hence what's said here should not be
taken as the last word on the subject. Perhaps a new edition is due, and would certainly be most welcome. However, for an excellent, accessible introduction to this exciting field,
this is still a great choice.
Rating:
Summary: Excellent Introduction
Review: 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 design and implementation of speech recognition systems, right from
isolated word recognition to large vocabulary continuous speech recognition
systems. Neural networks and their use in speech recognition is also presented,
though somewhat briefly. Rabiner was the author of the first widely-read tutorial on HMMs, so
naturally the presentation of HMMs is one of the strong points of this
textbook. The theory is developed in detail, but in an easy to follow
fashion, starting with the very basics and with plenty of helpful examples.
The implementation is discussed at great length as well, starting with
the simplest of tasks and progressing to the state-of-the-art (circa 1993). That isn't to say that HMMs are the only good part of this book - indeed,
practically every topic, whether it be perception, transforms, vector quantization
or dynamic programming, is presented with great clarity. This book really is easy to
learn from, with numerous examples and illustrations. The field of speech recognition is inherently multi-disciplinary in nature,
drawing upon various areas of study, including Physics, Physiology, Acoustics,
Signal Processing and Computer Science, to name but a few. The authors do a
great job of explaining all these facets, as well as the mathematics that
is an essential tool.
The only caveat is that it's now a little old (published 1993), since the
field has been growing by leaps and bounds - so while the basics remain
the same, things have changed and hence what's said here should not be
taken as the last word on the subject. Perhaps a new edition is due, and would certainly be most welcome. However, for an excellent, accessible introduction to this exciting field,
this is still a great choice.
Rating:
Summary: Classical Book for Speech recognition
Review: This ia a classical book on speech recognition. It covers the basic concepts and practical speech recognition Techniques. The first tutorial on HMM by Rabiner,appeared in IEEE, is included in this book with much more practical examples. This book helped me a lot during my post graduation and work in the area of speech recognition. Thanks to Rabiner and Juang !!!
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