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.

本文介绍一种基于模型的端到端语境自适应方法，它不依赖于解码器，适合用于设备上的个性化训练，实验结果表明，相比传统的重新评分技术，在连续个性化场景中，该方法可以使相对识别率（WER）提高 12％，实体提及特定 F1 得分提高 15.7％。