Graphical models for integrating syllabic information

We present graphical model based methodology that enhances a speech recognizer with information about syllabic segmentations. The segmentations are specified by locations of syllable nuclei, and the graphical models are able to consider these locations as “soft” information. The graphs give improved discrimination between speech and noise when compared to a baseline model. When using locations derived from oracle information an overall improvement is shown, and when the oracle syllable nuclei are augmented with information about lexical stress the methods give additional improvements over locations alone.