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. Code is available at https://github.com/owaski/WACO.

翻译：端到端语音翻译（E2E ST）旨在将源语音直接翻译为目标文本。现有语音翻译方法在仅有极少量语音-文本数据用于训练时表现较差。我们观察到，语音翻译模型的性能与语音和源转录文本之间的嵌入相似性密切相关。在本文中，我们提出了单词对齐对比学习（WACO），一种简单且有效的极端低资源语音到文本翻译方法。我们的核心思想是通过对比学习桥接语音和文本模态的单词级表示。我们在广泛使用的语音翻译基准数据集MuST-C以及来自IWSLT 2023的低资源方向马耳他语-英语上评估了WACO和其他方法。实验表明，仅使用1小时的并行语音-文本数据，WACO在BLEU得分上比最佳基线高出9分以上。代码可在https://github.com/owaski/WACO获取。