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Title:KL-Divergence Guided Two-Beam Viterbi Algorithm on Factorial HMMs
Author(s):Yeh, Raymond
Contributor(s):Hasegawa-Johnson, Mark
Subject(s):factorial hidden Markov model
Viterbi beam
digit recognition
Abstract:This thesis addresses the problem of the high computation complexity issue that arises when decoding hidden Markov models (HMMs) with a large number of states. A novel approach, the two-beam Viterbi, with an extra forward beam, for decoding HMMs is implemented on a system that uses factorial HMM to simultaneously recognize a pair of isolated digits on one audio channel. The two-beam Viterbi algorithm uses KL-divergence and hierarchical clustering to reduce the overall decoding complexity. This novel approach achieves 60% less computation compared to the baseline algorithm, the Viterbi beam search, while maintaining 82.5% recognition accuracy.
Issue Date:2014-05
Date Available in IDEALS:2014-10-31

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