CopyNE: Better Contextual ASR by Copying Named Entities

Anonymous

Abstract: End-to-end automatic speech recognition (ASR) systems have made significant progress in general scenarios. However, it remains challenging to transcribe contextual named entities (NEs) in the contextual ASR scenario. Previous approaches have attempted to address this by utilizing the NE dictionary. These approaches treat entities as individual tokens and generate them token-by-token, which may result in incomplete transcriptions of entities. In this paper, we treat entities as indivisible wholes and introduce the idea of copying into ASR. We design a systematic mechanism called CopyNE, which can copy entities from the NE dictionary. By copying all tokens of an entity at once, we can reduce errors during entity transcription, ensuring the completeness of the entity. Experiments demonstrate that CopyNE consistently improves the accuracy of transcribing entities compared to previous approaches. Even when based on the strong Whisper, CopyNE still achieves notable improvements.

Paper Type: long

Research Area: Speech recognition, text-to-speech and spoken language understanding

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: Chinese,English

Consent To Share Submission Details: On behalf of all authors, we agree to the terms above to share our submission details.