Efficient training algorithms for HMMs using incremental estimation
نویسندگان
چکیده
منابع مشابه
Efficient training algorithms for HMMs using incremental estimation
Typically, parameter estimation for a hidden Markov model (HMM) is performed using an expectation-maximization (EM) algorithm with the maximum-likelihood (ML) criterion. The EM algorithm is an iterative scheme which is well-deened and numerically stable, but convergence may require a large number of iterations. For speech recognition systems utilizing large amounts of training material, this re...
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Conventional training of a hidden Markov model (HMM) is performed by an expectation-maximization algorithm using a maximum likelihood (ML) criterion. Recently it was reported that, using an incremental variant of maximum a posteriori estimation, substantial speed improvements could be obtained. The approach requires a prior distribution when the training starts, although it is diicult to nd an ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Speech and Audio Processing
سال: 1998
ISSN: 1063-6676
DOI: 10.1109/89.725320