Knowledge Prompt for Whisper: An ASR Entity Correction Approach with Knowledge Base

Entity correction is crucial in Automatic Speech TABLE I Recognition (ASR), since erroneous entities seriously affect our understanding of ASR results. In this paper, in order to correct entity errors, we propose a knowledge prompt approach for Whisper (a recent ASR model trained with a corpus containing 680k hours of labeled speech recorded in various conditions). For a given audio, our approach consists of three steps: (1) obtaining its ASR result by Whisper; (2) fuzzy matching the ASR result with a knowledge base to obtain candidate entities; (3) using the candidate entities as a prompt to obtain the final ASR result by Whisper again. We conduct experiments on the test dataset of open-source Chinese speech corpus AISHELLNER. Experimental results show that our approach not only significantly improves the entity recall rate in ASR results (from 70.97% to 84.82%), but also reduces the overall Character Error Rate (CER).