Statistical Methods for Speech Recognition

by Frederick Jelinek
Statistical Methods for Speech Recognition cover
Good Books rating 4.07
Science and Innovation
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Technical
  • ID: 8370
  • Added: 2025-12-21
  • Updated: 2025-12-21
  • ISBN: 9780262546607
  • Publisher: MIT Press
  • Published: 2022-11-01
  • Reviews: 3

This book is a culmination of decades of research on the mathematical principles underlying speech recognition. It delves into essential statistical methods such as hidden Markov models, decision trees, and the expectation-maximization algorithm, presenting these concepts in a clear and accessible manner. The author's aim is to demonstrate the power of self-organization from real data and equip readers with the tools to apply these techniques effectively. /n/nFocusing on information theoretic goodness criteria, maximum entropy probability estimation, and smoothing of probability distributions, this book serves as a valuable resource for anyone interested in the intersection of statistics and speech recognition. It bridges the gap between theoretical principles and practical applications, making it an indispensable guide for researchers and practitioners in the field.

Reviews
ResearchGate · Eric Neufeld · 1999-01-01
technical 4.50

This review highlights the book's significant contributions to the field of speech recognition, particularly in its mathematical rigor. It is praised for its depth and technical accuracy.

Eric Neufeld's review on ResearchGate emphasizes the book's substantial impact on the field of speech recognition. The review commends the author for the mathematical rigor and technical precision, making it an essential read for those interested in the theoretical aspects of speech recognition. Neufeld notes that the book's detailed explanations and comprehensive coverage of statistical methods are particularly noteworthy. However, he also points out that the book's complexity may be challenging for those without a strong background in mathematics and statistics.

Quick quotes

    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 and dynamic programming.

    A valuable resource for those in the field of speech recognition.

ACM Digital Library · AM Andrew · 1999-01-01
critical 4.00

This review provides a critical analysis of the book, noting its strengths in theoretical depth and its limitations in practical applicability. It is a detailed and insightful critique.

AM Andrew's review in the ACM Digital Library offers a balanced critique of the book. Andrew acknowledges the book's strengths in providing a deep theoretical understanding of speech recognition techniques, particularly the use of statistical methods. However, the review also points out that the book's focus on theory may limit its practical applicability for some readers. Andrew suggests that while the book is an invaluable resource for researchers and academics, it may not be as useful for practitioners looking for immediate practical solutions. Overall, the review highlights the book's significance in the field while also noting its limitations.

Quick quotes

    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 and dynamic programming.

    A valuable resource for those in the field of speech recognition.

Goodreads · 1998-01-16
informative 3.70

This book is a comprehensive guide to the mathematical foundations of speech recognition, focusing on statistical techniques. The depth of research and technical detail make it a valuable resource for those in the field.

The book delves into the intricate world of speech recognition, providing a thorough exploration of the statistical methods that underpin this technology. It is a dense and technical read, but for those interested in the mathematical foundations of speech recognition, it offers a wealth of information. The author's expertise shines through, making it a valuable resource for academics and professionals in the field. However, it may not be accessible to casual readers due to its advanced content.

Quick quotes

    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 and dynamic programming.

    A valuable resource for those in the field of speech recognition.

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