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 [BibTeX] [Marc21]
Dynamic Bayesian Network Based Speech Recognition with Pitch and Energy as Auxiliary Variables
Type of publication: Conference paper
Citation: stephenson02c
Booktitle: 2002 IEEE International Workshop on Neural Networks for for Signal Processing (NNSP~2002)
Year: 2002
Month: 9
Address: Martigny, Switzerland
Crossref: stephenson02b:
Abstract: Pitch and energy are two fundamental features describing speech, having importance in human speech recognition. However, when incorporated as features in automatic speech recognition (ASR,',','), they usually result in a significant degradation on recognition performance due to the noise inherent in estimating or modeling them. In this paper, we show experimentally how this can be corrected by either conditioning the emission distributions upon these features or by marginalizing out these features in recognition. Since this is not obvious to do with standard hidden Markov models (HMMs,',','), this work has been performed in the framework of dynamic Bayesian networks (DBNs,',','), resulting in more flexibility in defining the topology of the emission distributions and in specifying whether variables should be marginalized out.
Userfields: ipdmembership={speech},
Projects Idiap
Authors Stephenson, Todd Andrew
Escofet, Jaume
Magimai.-Doss, Mathew
Bourlard, Hervé
Added by: [UNK]
Total mark: 0
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