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arXiv · 2110.02220

Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition

Tsendsuren Munkhdalai·Khe Chai Sim·Angad Chandorkar·Fan Gao·Mason Chua·Trevor Strohman·Françoise Beaufays
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Abstract

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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Tsendsuren Munkhdalai, Khe Chai Sim, Angad Chandorkar, Fan Gao, Mason Chua, Trevor Strohman, Françoise Beaufays. 2021-10-05. Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition. https://arxiv.org/abs/2110.02220

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