A study on knowledge source integration for candidate rescoring in automatic speech recognition

We propose a rescoring framework for speech recognition that

incorporates acoustic phonetic knowledge sources. The scores

corresponding to all knowledge sources are generated from a

collection of neural network based classifiers. Rescoring is then

performed by combining different knowledge scores and uses

them to reorder candidate strings provided by state-of-the-art

HMM-based speech recognizers. We report on continuous phone

recognition experiments using the TIMIT database. Our results

indicate that classifying manners and places of articulation

provides additional information in rescoring, and achieving

improved accuracies over our best baseline speech recognizers

using both context-independent and context-dependent phone

models. The same technique can also be extended to lattice

rescoring and large vocabulary continuous speech recognition.

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In  Proc. ICASSP

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