Bayesian learning of Gaussian mixture densities for hidden Markov models

1991 ◽  
Author(s):  
Jean-Luc Gauvain ◽  
Chin-Hui Lee
Keyword(s):  
Hidden Markov Models ◽  
Bayesian Learning ◽  
Markov Models ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Mixture Densities

Recognition of Isolated Digits Using Hidden Markov Models With Continuous Mixture Densities

1985 ◽  
Vol 64 (6) ◽  
pp. 1211-1234 ◽  
Author(s):  
L. R. Rabiner ◽  
B.-H. Juang ◽  
S. E. Levinson ◽  
M. M. Sondhi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Mixture Densities ◽  
Continuous Mixture

scholarly journals Hidden Markov Modeling of Human Pivoting

2007 ◽  
Vol 19 (4) ◽  
pp. 444-447 ◽  
Author(s):  
Yusuke Maeda ◽  
◽  
Tatsuya Ushioda ◽  
Keyword(s):  
Multidimensional Scaling ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Minimum Description Length ◽  
Human Movement ◽  
Gaussian Mixture ◽  
Markov Modeling ◽  
Metric Multidimensional Scaling ◽  
Hidden Markov Modeling

In modeling human movement using hidden Markov models (HMM), the “optimal” HMM with an appropriate number of states is determined based on the minimum description length (MDL) criterion. Human pivoting, typifying graspless manipulation, is modeled using Gaussian mixture HMMs. Analyzing the obtained HMMs using metric multidimensional scaling (MDS) showed the features of individual movement. Such dissimilarity analysis can be used to validate models of tacit skills in human manipulation.

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