LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
Xi Chen, Songyang Zhang, Qibing Bai, Kai Chen, Satoshi Nakamura
Abstract
We introduces ***LLaST***, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech translation (E2E ST) models by exploring model architecture design and optimization techniques tailored for LLMs. Our approach includes LLM-based speech translation architecture design, ASR-augmented training, multilingual data augmentation, and dual-LoRA optimization. Our approach demonstrates superior performance on the CoVoST-2 benchmark and showcases exceptional scaling capabilities powered by LLMs.We believe this effective method will serve as a strong baseline for speech translation and provide insights for futureimprovements of the LLM-based speech translation framework.- Anthology ID:
- 2024.findings-acl.416
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6976–6987
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.416
- DOI:
- Cite (ACL):
- Xi Chen, Songyang Zhang, Qibing Bai, Kai Chen, and Satoshi Nakamura. 2024. LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 6976–6987, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models (Chen et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.416.pdf