MaLa-ASR: Multimedia-Assisted LLM-Based ASR
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912112712876032 |
|---|---|
| author | Yang, Guanrou Ma, Ziyang Yu, Fan Gao, Zhifu Zhang, Shiliang Chen, Xie |
| author_facet | Yang, Guanrou Ma, Ziyang Yu, Fan Gao, Zhifu Zhang, Shiliang Chen, Xie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MaLa-ASR: Multimedia-Assisted LLM-Based ASR Yang, Guanrou Ma, Ziyang Yu, Fan Gao, Zhifu Zhang, Shiliang Chen, Xie Audio and Speech Processing Artificial Intelligence 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. |
| title | MaLa-ASR: Multimedia-Assisted LLM-Based ASR |
| topic | Audio and Speech Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2406.05839 |