Speech Translation
Speech translation (ST) aims to automatically convert spoken language in one language into written or spoken text in another, bridging communication barriers. Current research heavily utilizes large language models (LLMs) integrated with speech foundation models (SFMs), often employing techniques like chain-of-thought prompting and multimodal approaches to improve accuracy and reduce latency, particularly in simultaneous ST. These advancements are significant for improving cross-lingual communication in various applications, from real-time interpretation to accessibility tools, and are driving innovation in both model architectures and evaluation methodologies.
Papers
Improved Long-Form Spoken Language Translation with Large Language Models
Arya D. McCarthy, Hao Zhang, Shankar Kumar, Felix Stahlberg, Axel H. Ng
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations
Ioannis Tsiamas, José A. R. Fonollosa, Marta R. Costa-jussà
WACO: Word-Aligned Contrastive Learning for Speech Translation
Siqi Ouyang, Rong Ye, Lei Li