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A tutorial on hidden markov models and selected applications in speech recognition
 Proceedings of the IEEE
, 1989
"... 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 s ..."
Abstract

Cited by 4251 (1 self)
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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. I.
Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions
 Advances in Neural Information Processing Systems 9
, 1996
"... We present new algorithms for parameter estimation of HMMs. By adapting a framework used for supervised learning, we construct iterative algorithms that maximize the likelihood of the observations while also attempting to stay "close" to the current estimated parameters. We use a bound on the relati ..."
Abstract

Cited by 21 (4 self)
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We present new algorithms for parameter estimation of HMMs. By adapting a framework used for supervised learning, we construct iterative algorithms that maximize the likelihood of the observations while also attempting to stay "close" to the current estimated parameters. We use a bound on the relative entropy between the two HMMs as a distance measure between them. The result is new iterative training algorithms which are similar to the EM (BaumWelch) algorithm for training HMMs. The proposed algorithms are composed of a step similar to the expectation step of BaumWelch and a new update of the parameters which replaces the maximization (reestimation) step. The algorithm takes only negligibly more time per iteration and an approximated version uses the same expectation step as BaumWelch. We evaluate experimentally the new algorithms on synthetic and natural speech pronunciation data. For sparse models, i.e. models with relatively small number of nonzero parameters, the proposed alg...