Generative Annotation for ASR Named Entity Correction

ACL ARR 2025 February Submission1869 Authors

Abstract: End-to-end automatic speech recognition systems often fail to transcribe domain-speciffcnamed entities, causing catastrophic failuresin downstream tasks. Numerous fast and lightweight named entity correction (NEC) models have been proposed in recent years. These models, mainly leveraging phonetic-level edit distance algorithms, have shown impressive performances. However, when theforms of the wrongly-transcribed words(s) and the ground-truth entity are signiffcantly different, these methods often fail to locate the wrongly transcribed words in hypothesis, thus limiting their usage. We propose a novel NEC method that utilizes speech sound features to retrieve candidate entities. With speech sound features and candidate entities, we inovatively design a generative method to annotate entityerrors in ASR transcripts and replace the textwith correct entities. This method is effective inscenarios of word form difference. We test ourmethod using open-source and self-constructed test sets. The results demonstrate that our NEC method can bring signiffcant improvement to entity accuracy. We will open source our self constructed test set and training data.

Paper Type: Long

Research Area: Speech Recognition, Text-to-Speech and Spoken Language Understanding

Research Area Keywords: Speech Recognition, Speech Entity Error Correction

Contribution Types: Data resources

Languages Studied: Chinese, English

Submission Number: 1869