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Hidden Markov Models

  • Hervé A. Bourlard
  • Nelson Morgan
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 247)

Abstract

The most efficient approach developed so far to deal with the statistical variations of speech (in both the frequency and temporal domains) consists in modeling the lexicon words or the constituent speech units by Hidden Markov Models (HMMs) [Baker 1975a,; Jelinek 1976; Bahl et al.,1983; Levinson et al., 1983; Rabiner & Juang, 1986]. First described in [Baum & Petrie, 1966; Baum & Eagon, 1967; Baum, 1972], this formalism was proposed as a statistical method of estimating the probabilistic functions of Markov chains.’ Shortly afterwards, they were extended to automatic speech recognition independently at CMU [Baker, 1975b] and IBM [Bakis, 1976; Jelinek, 1976].

Keywords

Hide MARKOV Model Automatic Speech Recognition Dynamic Time Warping Emission Probability Continuous Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Hervé A. Bourlard
    • 1
    • 2
  • Nelson Morgan
    • 2
    • 3
  1. 1.Lernout & Hauspie Speech ProductsBelgium
  2. 2.International Computer Science InstituteBerkeleyUSA
  3. 3.University of CaliforniaBerkeleyUSA

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