Abstract
This work presents the use of dynamic Bayesian networks (DBNs) to jointly estimate word position and word identity in an automatic speech recognition system. In particular, we have augmented a standard Hidden Markov Model (HMM) with counts and locations of syllable nuclei. Three experiments are presented here. The first uses oracle syllable counts, the second uses oracle syllable nuclei locations, and the third uses estimated (non-oracle) syllable nuclei locations. All results are presented on the 10 and 500 word tasks of the SVitch-board corpus. The oracle experiments give relative improvements ranging from 7.0% to 37.2%. When using estimated syllable nuclei a relative improvement of 3.1% is obtained on the 10 word task.
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