WACO: Word-Aligned Contrastive Learning for Speech Translation
Siqi Ouyang, Rong Ye, Lei Li · 2023
End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text.Existing ST methods perform poorly when only extremely small speech-text data are available for training.We observe that an ST model's performance closely correlates with its embedding similarity between speech and source transcript.In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation.Our key idea is bridging word-level representations for both speech and text modalities via contrastive learning.We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark, and on a low-resource direction Maltese-English from IWSLT 2023.Our experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data.