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End-to-end Speech Translation (ST) aims to convert speech into target textwithin a unified model. The inherent differences between speech and textmodalities often impede effective cross-modal and cross-lingual transfer.Existing methods typically employ hard alignment (H-Align) of individual speechand text segments, which can degrade textual representations. To address this,we introduce Soft Alignment (S-Align), using adversarial training to align therepresentation spaces of both modalities. S-Align creates a modality-invariantspace while preserving individual modality quality. Experiments on threelanguages from the MuST-C dataset show S-Align outperforms H-Align acrossmultiple tasks and offers translation capabilities on par with specializedtranslation models.

Soft Alignment of Modality Space for End-to-end Speech Translation