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Statistical Methods for Speech Recognition (Hardcover) by Frederick Jelinek
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Key item features
- ISBN: 9780262100663
- Condition: New
- Hard cover
- Language: English
- Pages: 305
- Sewn binding. Cloth over boards. 305 p. Language, Speech, and Communication.
- This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.
Specs
- Book formatHardcover
- Fiction/nonfictionNon-Fiction
- GenreComputing & Internet
- Publication dateJanuary, 1998
- Pages305
- Reading levelUndergraduate
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9780262100663. New condition. Hard cover. Language: English. Pages: 305. Sewn binding. Cloth over boards. 305 p. Language, Speech, and Communication. This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.
- ISBN: 9780262100663
- Condition: New
- Hard cover
- Language: English
- Pages: 305
- Sewn binding. Cloth over boards. 305 p. Language, Speech, and Communication.
- This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.
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Specifications
Book format
Hardcover
Fiction/nonfiction
Non-Fiction
Genre
Computing & Internet
Publication date
January, 1998
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