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arXiv · 2312.10952

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

Yuhao Zhang·Kaiqi Kou·Bei Li·Chen Xu·Chunliang Zhang·Tong Xiao·Jingbo Zhu
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Abstract

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.

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Yuhao Zhang, Kaiqi Kou, Bei Li, Chen Xu, Chunliang Zhang, Tong Xiao, Jingbo Zhu. 2023-12-18. Soft Alignment of Modality Space for End-to-end Speech Translation. https://arxiv.org/abs/2312.10952

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