Hidden Markov Models
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].
KeywordsHide MARKOV Model Automatic Speech Recognition Dynamic Time Warping Emission Probability Continuous Speech Recognition
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