Fundamentals of speech recognition
Fundamentals of speech recognition
Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Topic transition detection using hierarchical hidden Markov and semi-Markov models
Proceedings of the 13th annual ACM international conference on Multimedia
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Hierarchical hidden Markov models for information extraction
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A discriminative model corresponding to hierarchical HMMs
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
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The generalized Viterbi algorithm, a direct extension of the Viterbi algorithm for hidden Markov models (HMMs), has been used to find the most likely state sequence for hierarchical HMMs. However, the generalized Viterbi algorithm finds the most likely whole level state sequence rather than the most likely upper level state sequence. In this paper, we propose a marginalized Viterbi algorithm, which finds the most likely upper level state sequence by marginalizing lower level state sequences. We show experimentally that the marginalized Viterbi algorithm is more accurate than the generalized Viterbi algorithm in terms of upper level state sequence estimation.