Towards interfacing large language models with ASR systems using confidence measures and prompting
Maryam Naderi, Enno Hermann, Alexandre Nanchen, Sevada Hovsepyan, Mathew Magimai. -Doss
7/31/2024
eess.AScs.CL
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Abstract
As large language models (LLMs) grow in parameter size and capabilities, such as interaction through prompting, they open up new ways of interfacing with automatic speech recognition (ASR) systems beyond rescoring n-best lists. This work investigates post-hoc correction of ASR transcripts with LLMs. To avoid introducing errors into likely accurate transcripts, we propose a range of confidence-based filtering methods. Our results indicate that this can improve the performance of less competitive ASR systems.
