MaLa-ASR: Multimedia-Assisted LLM-Based ASR
<|reference_start|>MaLa-ASR: Multimedia-Assisted LLM-Based ASR: As more and more information-rich data like video become available, utilizing multi-modal auxiliary information to enhance audio tasks has sparked widespread research interest. The recent surge in research on LLM-based audio models provides fresh perspectives for tackling audio tasks. Given that LLM can flexibly ingest multiple inputs, we propose MaLa-ASR, an LLM-based ASR model that can integrate textual keywords extracted from presentation slides to improve recognition of conference content. MaLa-ASR yields average WERs of 9.4% and 11.7% on the L95 and S95 subsets of the SlideSpeech corpus, representing a significant relative WER drop of 27.9% and 44.7% over the baseline model reported in SlideSpeech. MaLa-ASR underscores LLM's strong performance in speech tasks and the capability to integrate auxiliary information conveniently. By adding keywords to the input prompt, the biased word error rate (B-WER) reduces relatively by 46.0% and 44.2%, establishing a new SOTA on this dataset.<|reference_end|>
@article{yang2024mala-asr:,
    title={MaLa-ASR: Multimedia-Assisted LLM-Based ASR},
    author={Guanrou Yang, Ziyang Ma, Fan Yu, Zhifu Gao, Shiliang Zhang, Xie Chen},
    journal={arXiv preprint arXiv:2406.05839},
    year={2024},
    doi={10.21437/Interspeech.2024},
    archivePrefix={arXiv},
    eprint={2406.05839},
    primaryClass={eess.AS cs.AI}
}