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.
BibTeX
@inproceedings{chen-etal-2024-llast,
title = "{LL}a{ST}: Improved End-to-end Speech Translation System Leveraged by Large Language Models",
author = "Chen, Xi and
Zhang, Songyang and
Bai, Qibing and
Chen, Kai and
Nakamura, Satoshi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.416/",
doi = "10.18653/v1/2024.findings-acl.416",
pages = "6976--6987"
}