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.
Bradford Books imprint
Frederick Jelinek is Julian Sinclair Smith Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, where he is also Director for the Center for Language and Speech Processing.
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.
Bradford Books imprint
Author
Frederick Jelinek is Julian Sinclair Smith Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, where he is also Director for the Center for Language and Speech Processing.
