Lexical Error Guard: Leveraging Large Language Models for Enhanced ASR Error Correction

Error correction is a vital element in modern automatic speech recognition (ASR) systems. A significant portion of ASR error correction work is closely integrated within specific ASR systems, which creates challenges for adapting these solutions to different ASR frameworks. This research introduces Lexical Error Guard (LEG), which leverages the extensive pre-trained knowledge of large language models (LLMs) and employs instructional learning to create an adaptable error correction system compatible with various ASR platforms. Additionally, a parameter-efficient fine-tuning method is utilized using quantized low-rank adaptation (QLoRA) to facilitate fast training of the system. Tested on the LibriSpeech data corpus, the results indicate that LEG improves ASR results when used with various Whisper model sizes. Improvements in WER are made, with a decrease from 2.27% to 2.21% on the “Test Clean” dataset for Whisper Large with beam search. Improvements on the “Test Other” dataset for Whisper Large with beam search are also made, from 4.93% to 4.72%.