Rating: 
Summary: all advanced students in speech science -- don't miss it!
Review: A really fine textbook for advanced students and researchers. It could profitably enhance graduate course sequences following my own text "Acoustics of Speech Communication: Fundamentals, Speech Perception Theory and Technology", Allyn & Bacon, 1999, ISBN-0-205-19887-2.
Rating: 
Summary: Not bad but overrated: broad and shallow
Review: GENERAL IDEA: Broad coverage but it lacks depth and details - particularly practical details. That is, the presentation is often too sketchy, mainly because it approaches too many subjects for its available space. I would not say that this book is strong on theory either. It is quite obvious that it avoids getting too formal and rigurous, probably to remain attractive for non-specialists too. CASE STUDY: One specific problem I had with the Hidden Markov Models, that are supperficially presented (or spread I could say) in several separate sections of the book, so it's not been a pleasure trying to actually understand them properly and completely as a fundamental concept, to make them work in my particular application. TITLE: The book's title IS misleading because it starts with "Speeech" and this book's main subject is not speech but (written) language. Actually there are only a few chapters on speech. CONCLUSION: Get this book if you are looking for a good overview of the field. As soon as you need in-depth coverage of some particular topic you will look for additional resources.
Rating: 
Summary: Good, but many errors
Review: I recently had reason to return to Jurafsky and Martin's* "Speech and Language Processing" to do a little brush-up on pronunciation models. Of course, I got diverted; this time by an insightful review of the "internal structure" of words. I came away reminded of why this is perhaps the single best textbook I've ever read. "Speech and Language Processing" is always the first source I check, and it is quite often the last. First of all, Jurafsky and Martin cover absolutely everything you need to know in order to understand the state of the art systems and to read primary sources such as journals or conference proceedings. You could teach an advanced undergraduate or graduate course by simply tackling it a chapter at a time and discussing everyone's solutions to the exercises. The book is organized by interleaving theoretical topics, such as regular expressions and automata, with practical applications, such as pronunciation modeling or pattern matching. This allows for a fast start on interesting and realistic applications while providing a solid foundation for understanding the field. Second, the book is not only readable, it's enjoyable. The examples are clever, not cute or forced. The topics flow from one to the next in an almost seamless narrative. Third, the book is scholarly to the point of lacing pages with references to original sources. Somehow, Jurafsky and Martin have managed to track down fascinating threads such as the development of the currently accepted statistical models for speech recognition. Fourth, and most amazingly, Jurafsky and Martin manage all of this while maintaining a rigorous standard of definition and example that should be a model to the rest of the field. Terms are defined when they're used or cross-referenced. Algorithms are given in well defined and carefully crafted pseudo-code (using pseudocode neatly leapfrogged two decades of computational linguistics books tied to obscure programming languages). For instance, their definition of CYK parsing is a minimal, elegant nesting of for-loops from which the complexity of the algorithm is self-evident. Speaking of rigor, the book is very well copy edited, typeset, and indexed. This book isn't the last book you'll need; it's the first. Jurafsky and Martin open the door to the cognitive sciences, including linguistics, psychology and philosophy, and the computer sciences including logic, automata, formal languages, algorithms, and statistical estimation. Not to mention artificial intelligence; all the good problems are AI-complete**, after all, and Jurafsky and Martin don't let you forget it. --------------------
* There were actually several other chapter authors, including Keith Vander Linden on Natural Language Generation, Nigel Ward on Machine Translation, and Andy Kehler on Discourse; it's a tribute to all of them that the book hangs together so well.
** "AI-complete", a term derived from "NP-complete" and "Turing-complete", applies to a problem that is so hard that if you solved it, you could solve any other interesting artificial intelligence problem in terms of the solution to your problem.
Rating:
Summary: Readable, Rigorous, Thorough and Scholarly
Review: I recently had reason to return to Jurafsky and Martin's* "Speech and Language Processing" to do a little brush-up on pronunciation models. Of course, I got diverted; this time by an insightful review of the "internal structure" of words. I came away reminded of why this is perhaps the single best textbook I've ever read. "Speech and Language Processing" is always the first source I check, and it is quite often the last. First of all, Jurafsky and Martin cover absolutely everything you need to know in order to understand the state of the art systems and to read primary sources such as journals or conference proceedings. You could teach an advanced undergraduate or graduate course by simply tackling it a chapter at a time and discussing everyone's solutions to the exercises. The book is organized by interleaving theoretical topics, such as regular expressions and automata, with practical applications, such as pronunciation modeling or pattern matching. This allows for a fast start on interesting and realistic applications while providing a solid foundation for understanding the field. Second, the book is not only readable, it's enjoyable. The examples are clever, not cute or forced. The topics flow from one to the next in an almost seamless narrative. Third, the book is scholarly to the point of lacing pages with references to original sources. Somehow, Jurafsky and Martin have managed to track down fascinating threads such as the development of the currently accepted statistical models for speech recognition. Fourth, and most amazingly, Jurafsky and Martin manage all of this while maintaining a rigorous standard of definition and example that should be a model to the rest of the field. Terms are defined when they're used or cross-referenced. Algorithms are given in well defined and carefully crafted pseudo-code (using pseudocode neatly leapfrogged two decades of computational linguistics books tied to obscure programming languages). For instance, their definition of CYK parsing is a minimal, elegant nesting of for-loops from which the complexity of the algorithm is self-evident. Speaking of rigor, the book is very well copy edited, typeset, and indexed. This book isn't the last book you'll need; it's the first. Jurafsky and Martin open the door to the cognitive sciences, including linguistics, psychology and philosophy, and the computer sciences including logic, automata, formal languages, algorithms, and statistical estimation. Not to mention artificial intelligence; all the good problems are AI-complete**, after all, and Jurafsky and Martin don't let you forget it. --------------------
* There were actually several other chapter authors, including Keith Vander Linden on Natural Language Generation, Nigel Ward on Machine Translation, and Andy Kehler on Discourse; it's a tribute to all of them that the book hangs together so well.
** "AI-complete", a term derived from "NP-complete" and "Turing-complete", applies to a problem that is so hard that if you solved it, you could solve any other interesting artificial intelligence problem in terms of the solution to your problem.
Rating:
Summary: An excellent introduction to NLP...
Review: I started reading James Allen's Natural Language Understanding to get background information on an NLP indepedent study project. The book was good, but I still found some points unclear and turned to Jurafsky/Martin for more information. In the end I found Jurafsky very comprehensive and much more down to earth than Allen. (They make useful references to popular movies and culture without sacrificing their academic reputation.) The work introduces basic NLP concepts as Allen does, but then presents applications that continually refer back to the methods. For example, Allen explains the Viterbi algorithm as a method for tagging sentences. Jurafsky/Martin present it, then refer to it in applications such as spell checking, voice recognition, and sentence tagging. The book also serves as a useful guide to finding the more significant NLP papers for further research. If you're interested in NLP this is an excellent place to start!
Rating:
Summary: I looked for
Review: something which I can use - I am a linguist - and found it immensly readable and useful
Rating:
Summary: The Book is a Masterpiece
Review: The book showcases a comprehensive and user-friendly approach to cover the leading research in the field of Natural Language Processing and Speech Recognition. It mingles theories and applications to demonstrate the full developmental cycle of computational aspect of NLP. It is a MUST-have for those who can afford only one book but desire to learn virtually aspects of computational linguistics.
Rating:
Summary: A Landmark Book
Review: The previous best book on NLP was James Allen's (1995), which was considered ambitious at the time because it covered syntax, semantics and some pragmatics. But Martin and Jurafsky is far more ambitious, because it covers speech recognition as well, and has far expanded coverage of language generation and translation. It also covers the great advances in statistical techniques that have marked the last decade. It is a beautiful synthesis that will reward the experienced expert in the field with new insights and new connections in the form of historical notes that are not well known. And it is well-written and clear enough that even the beginning student can follow it through. Before this book, you would have had to read Allen's book, Charniak's short book on statistical NLP, something on speech recognition, and something else on generation and translation. Like squeezing clowns into a circus car, Jurafsky and Martin somehow, improbably, manage to squeeze this all into one book, but in a way that is elegant and holds together perfectly; not at all the hodge-podge that one might expect. I expect that this book will be seen as one of the landmarks that pushes the field forward. It's worth comparing this book to the other recent NLP text: Manning and Shutze. Jurafsky and Martin cover much more ground, including many aspects that are ignored by Manning and Schutze. So if you want a general overview of natural language, if you want to know about the syntax of English, or the intricacies of dialog, if you are teaching or taking a general NLP course, then Jurafsky and Martin is the one for you. But if your needs are more focused on the algorithms for lower-level text processing with statistical techniques, or if you want to build a specific practical application, then Manning and Schutze is far more comprehensive and likely to have your answer. If you're a serious student or professional in NLP, you just have to have both.
Rating:
Summary: Excellent Starting Point
Review: This book covers a wide range of speech and liguistics related material and does a very good job in guiding the reader to up to date specialized research in each field. Obviously, given the enormously ambitious scope, a single book cannot cover any of its areas in depth, but it serves as an ideal starting point for further exploration.
Rating: 
Summary: This is not a speech book
Review: This book has a good coverage on NLP but not speech. The title is misleading.
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