Towards interfacing large language models with ASR systems using confidence measures and prompting

• URL: http://arxiv.org/abs/2407.21414v1
• Date: Wed, 31 Jul 2024 08:00:41 GMT
• Title: Towards interfacing large language models with ASR systems using confidence measures and prompting
• Authors: Maryam Naderi, Enno Hermann, Alexandre Nanchen, Sevada Hovsepyan, Mathew Magimai. -Doss,
• Abstract summary: This work investigates post-hoc correction of ASR transcripts with large language models (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.
• Score: 54.39667883394458
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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