Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Second the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to carefully and methodically review the theoretical aspects of this type of statistical modeling and show how they have been applied to selected problems in machine recognition of speech.
In 1989, Rabiner reviewed the theoretical aspects of Hidden Markov Modeling and applied it to the selected problems in machine recognition of speech REF .
A tutorial on hidden Markov models and selected applications in speech recognition
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Mathematics Computer Science