Speech recognition with dynamic Bayesian networks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Speaking in shorthand — a syllable-centric perspective for understanding pronunciation variation
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Integrating Syllable Boundary Information Into Speech Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Incorporating information from syllable-length time scales into automatic speech recognition
Incorporating information from syllable-length time scales into automatic speech recognition
Speech recognition with dynamic bayesian networks
Speech recognition with dynamic bayesian networks
Regularized adaptation: theory, algorithms and applications
Regularized adaptation: theory, algorithms and applications
Graphical models for large vocabulary speech recognition
Graphical models for large vocabulary speech recognition
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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.