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 [BibTeX] [Marc21]
Mixed Bayesian Networks with Auxiliary Variables for Automatic Speech Recognition
Type of publication: Idiap-RR
Citation: stephenson01c
Number: Idiap-RR-45-2001
Year: 2001
Institution: IDIAP
Note: In ``International Conference on Pattern Recognition (ICPR~2002)'', 2002
Abstract: Standard hidden Markov models (HMMs,',','), as used in automatic speech recognition (ASR,',','), calculate their emission probabilities by an artificial neural network (ANN) or a Gaussian distribution conditioned on the hidden state variable, considering the emissions independent of any other variable in the model. Recent work showed the benefit of conditioning the emission distributions on a discrete auxiliary variable, which is observed in training and hidden in recognition. Related work has shown the utility of conditioning the emission distributions on a continuous auxiliary variable. We apply mixed Bayesian networks (BNs) to extend these works by introducing a continuous auxiliary variable that is observed in training but is hidden in recognition. We find that an auxiliary pitch variable conditioned itself upon the hidden state can degrade performance unless the auxiliary variable is also hidden. The performance, furthermore, can be improved by making the auxiliary pitch variable independent of the hidden state.
Userfields: ipdmembership={speech},
Projects Idiap
Authors Stephenson, Todd Andrew
Magimai.-Doss, Mathew
Bourlard, Hervé
Crossref by stephenson02a
Added by: [UNK]
Total mark: 0
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