CopyNE: Better Contextual ASR by Copying Named Entities
Shilin Zhou Zhenghua Li Yu Hong Min Zhang Zhefeng Wang Baoxing Huai
Recent years have seen remarkable progress in automatic speech recognition
(ASR). However, traditional token-level ASR models have struggled with
accurately transcribing entities due to the problem of homophonic and
near-homophonic tokens. This paper introduces a novel approach called CopyNE,
which uses a span-level copying mechanism to improve ASR in transcribing
entities. CopyNE can copy all tokens of an entity at once, effectively avoiding
errors caused by homophonic or near-homophonic tokens that occur when
predicting multiple tokens separately. Experiments on Aishell and ST-cmds
datasets demonstrate that CopyNE achieves significant reductions in character
error rate (CER) and named entity CER (NE-CER), especially in entity-rich
scenarios. Furthermore, even when compared to the strong Whisper baseline,
CopyNE still achieves notable reductions in CER and NE-CER. Qualitative
comparisons with previous approaches demonstrate that CopyNE can better handle
entities, effectively improving the accuracy of ASR.