Abstract:In this paper, we derive an algorithm based on well-known BW algorithm for estimating the parameters of a hidden Markov model (we call it SBW algorithm). SBW algorithm relies on a low dimensional stat...In this paper, we derive an algorithm based on well-known BW algorithm for estimating the parameters of a hidden Markov model (we call it SBW algorithm). SBW algorithm relies on a low dimensional state-specific feature set rather than rely on a common high dimensional feature set as conventional BW algorithm, so avoiding directly estimating high-dimensional probability density functions in HMM training. Our computer simulation example shows that the performance of the new algorithm is superior (over) the conventional Baum-Welch algorithm.Read More
Publication Year: 2003
Publication Date: 2003-01-01
Language: en
Type: article
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Abstract: In this paper, we derive an algorithm based on well-known BW algorithm for estimating the parameters of a hidden Markov model (we call it SBW algorithm). SBW algorithm relies on a low dimensional state-specific feature set rather than rely on a common high dimensional feature set as conventional BW algorithm, so avoiding directly estimating high-dimensional probability density functions in HMM training. Our computer simulation example shows that the performance of the new algorithm is superior (over) the conventional Baum-Welch algorithm.