Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition. (arXiv:2110.02220v1 [eess.AS])

Fast contextual adaptation has shown to be effective in improving Automatic
Speech Recognition (ASR) of rare words and when combined with an on-device
personalized training, it can yield an even better recognition result. However,
the traditional re-scoring approaches based on an external language model is
prone to diverge during the personalized training. In this work, we introduce a
model-based end-to-end contextual adaptation approach that is decoder-agnostic
and amenable to on-device personalization. Our on-device simulation experiments
demonstrate that the proposed approach outperforms the traditional re-scoring
technique by 12% relative WER and 15.7% entity mention specific F1-score in a
continues personalization scenario.



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