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Learning from past mistakes: improving automatic speech recognition output via noisy-clean phrase context modeling
2019
APSIPA Transactions on Signal and Information Processing
Automatic speech recognition (ASR) systems often make unrecoverable errors due to subsystem pruning (acoustic, language and pronunciation models); for example, pruning words due to acoustics using short-term context, prior to rescoring with long-term context based on linguistics. In this work, we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt
doi:10.1017/atsip.2018.31
fatcat:pjrqvdkvszgi7nshq6h4bpu43y