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Towards interfacing large language models with ASR systems using confidence measures and prompting (2407.21414v1)

Published 31 Jul 2024 in eess.AS and cs.CL

Abstract: As 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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Authors (5)
  1. Maryam Naderi (1 paper)
  2. Enno Hermann (8 papers)
  3. Alexandre Nanchen (1 paper)
  4. Sevada Hovsepyan (2 papers)
  5. Mathew Magimai. -Doss (16 papers)