@inproceedings{4cc25801c9634cdcba0efedc48ef9b0b,
title = "Training algorithms for hidden Markov models using entropy based distance functions",
abstract = "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 (Baum-Welch) algorithm for training HMMs. The proposed algorithms are composed of a step similar to the expectation step of Baum-Welch and a new update of the parameters which replaces the maximization (re-estimation) step. The algorithm takes only negligibly more time per iteration and an approximated version uses the same expectation step as Baum-Welch. We evaluate experimentally the new algorithms on synthetic and natural speech pronunciation data. For sparse models, i.e. models with relatively small number of non-zero parameters, the proposed algorithms require significantly fewer iterations.",
author = "Yoram Singer and Warmuth, \{Manfred K.\}",
year = "1997",
language = "English (US)",
isbn = "0262100657",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "641--647",
booktitle = "Advances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996",
note = "10th Annual Conference on Neural Information Processing Systems, NIPS 1996 ; Conference date: 02-12-1996 Through 05-12-1996",
}