Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training
نویسندگان
چکیده
منابع مشابه
Adjusted Viterbi training for hidden Markov models
We consider estimation of the emission parameters in hidden Markov models. Commonly, one uses the EM algorithm for this purpose. However, our primary motivation is the Philips speech recognition system wherein the EM algorithm is replaced by the Viterbi training algorithm. Viterbi training is faster and computationally less involved than EM, but it is also biased and need not even be consistent...
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The EM procedure is a principal tool for parameter estimation in the hidden Markov models. However, applications replace EM by Viterbi extraction, or training (VT). VT is computationally less intensive, more stable and has more of an intuitive appeal, but VT estimation is biased and does not satisfy the following fixed point property. Hypothetically, given an infinitely large sample and initial...
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Hidden Markov models (HMM) are well known in speech recognition, where they are trained to recognize spoken words and even whole sentences. They are used to find the parameters of a so-called hidden model (usually a DTMC) by training it with observed output sequences. This paper introduces an approach to train stochastic Petri nets with the methods of HMM. As opposed to a DTMC, a stochastic Pet...
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ژورنال
عنوان ژورنال: Algorithms for Molecular Biology
سال: 2010
ISSN: 1748-7188
DOI: 10.1186/1748-7188-5-38