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

• URL: http://arxiv.org/abs/2110.02220v2
• Date: Thu, 7 Oct 2021 00:12:51 GMT
• Title: Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition
• Authors: Tsendsuren Munkhdalai, Khe Chai Sim, Angad Chandorkar, Fan Gao, Mason Chua, Trevor Strohman, Fran\c{c}oise Beaufays
• Abstract summary: 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.
• Score: 16.367495908535894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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