End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model. The inherent differences between speech and text modalities often impede effective cross-modal and cross-lingual transfer. Existing methods typically employ hard alignment (H-Align) of individual speech and text segments, which can degrade textual representations. To address this, we introduce Soft Alignment (S-Align), using adversarial training to align the representation spaces of both modalities. S-Align creates a modality-invariant space while preserving individual modality quality. Experiments on three languages from the MuST-C dataset show S-Align outperforms H-Align across multiple tasks and offers translation capabilities on par with specialized translation models.
@article{arxiv.2312.10952,
title = {Soft Alignment of Modality Space for End-to-end Speech Translation},
author = {Yuhao Zhang and Kaiqi Kou and Bei Li and Chen Xu and Chunliang Zhang and Tong Xiao and Jingbo Zhu},
journal= {arXiv preprint arXiv:2312.10952},
year = {2023}
}