Towards interfacing large language models with ASR systems using confidence measures and prompting
<|reference_start|>Towards interfacing large language models with ASR systems using confidence measures and prompting: 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.<|reference_end|>
@article{naderi2024towards,
    title={Towards interfacing large language models with ASR systems using
  confidence measures and prompting},
    author={Maryam Naderi, Enno Hermann, Alexandre Nanchen, Sevada Hovsepyan,
  Mathew Magimai.-Doss},
    journal={Proc. Interspeech 2024, 2980-2984},
    year={2024},
    doi={10.21437/Interspeech.2024-989},
    archivePrefix={arXiv},
    eprint={2407.21414},
    primaryClass={eess.AS cs.CL}
}