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arXiv · 2407.21414

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

Maryam Naderi·Enno Hermann·Alexandre Nanchen·Sevada Hovsepyan·Mathew Magimai. -Doss
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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.

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Maryam Naderi, Enno Hermann, Alexandre Nanchen, Sevada Hovsepyan, Mathew Magimai. -Doss. 2024-07-31. Towards interfacing large language models with ASR systems using confidence measures and prompting. https://doi.org/10.21437/interspeech.2024-989

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