The adjusted Viterbi training for hidden Markov models
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...
متن کاملThe adjusted Viterbi training for hidden Markov models
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...
متن کاملHidden Markov Models and the Viterbi algorithm
is understood to have N hidden Markov states labelled by i (1 ≤ i ≤ N), and M possible observables for each state, labelled by a (1 ≤ a ≤ M). The state transition probabilies are pij = p(qt+1 = j | qt = i), 1 ≤ i, j ≤ N (where qt is the hidden state at time t), the emission probability for the observable a from state i is ei(a) = p(Ot = a | qt = i) (where Ot is the observation at time t), and t...
متن کاملm at h . ST ] 1 4 Se p 20 07 Adjusted Viterbi training for hidden Markov models
To estimate the emission parameters in hidden Markov models one commonly uses the EM algorithm or its variation. Our primary motivation, however, 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. We propose an...
متن کاملAdjusted Viterbi Training
We propose modifications of the Viterbi Training (VT) algorithm to estimate emission parameters in Hidden Markov Models (HMM) which are widely used in speech recognition, natural language modeling, image analysis, and bioinformatics. Our goal is to alleviate the inconsistency of VT while controlling the amount of extra computations. Specifically, we modify VT to enable it asymptotically to fix ...
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ژورنال
عنوان ژورنال: Bernoulli
سال: 2008
ISSN: 1350-7265
DOI: 10.3150/07-bej105