Contextual biasing is an important and challenging
\ntask for end-to-end automatic speech recognition (ASR) systems,
\nwhich aims to achieve better recognition performance by biasing
\nthe ASR system to particular context phrases such as person
\nnames, music list, proper nouns, etc. Existing methods mainly
\ninclude contextual LM biasing and adding bias encoder into
\nend-to-end ASR models. In this work, we introduce a novel
\napproach to do contextual biasing by adding a contextual spelling
\ncorrection model on top of the end-to-end ASR system. We
\nincorporate contextual information into a sequence-to-sequence
\nspelling correction model with a shared context encoder. The proposed
\nmodel includes two different mechanisms: autoregressive
\n(AR) and non-autoregressive (NAR). We also propose filtering
\nalgorithms to handle large-size context lists, and performance
\nbalancing mechanisms to control the biasing degree of the model.
\nThe proposed model is a general biasing solution which is
\ndomain-insensitive and can be adopted in different scenarios.
\nExperiments show that the proposed method achieves as much
\nas 51% relative word error rate (WER) reduction over ASR
\nsystem and outperforms traditional biasing methods. Compared
\nto the AR solution, the NAR model reduces model size by 43.2%
\nand speeds up inference by 2.1 times.<\/p>\n","protected":false},"excerpt":{"rendered":"
Contextual biasing is an important and challenging task for end-to-end automatic speech recognition (ASR) systems, which aims to achieve better recognition performance by biasing the ASR system to particular context phrases such as person names, music list, proper nouns, etc. Existing methods mainly include contextual LM biasing and adding bias encoder into end-to-end ASR models. 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