STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation

How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data? Existing techniques often attempt to transfer powerful machine translation (MT) capabilities to ST, but neglect the representation discrepancy across modalities. In this paper, we propose the Speech-TExt Manifold Mixup (STEMM) method to calibrate such discrepancy. Specifically, we mix up the representation sequences of different modalities, and take both unimodal speech sequences and multimodal mixed sequences as input to the translation model in parallel, and regularize their output predictions with a selflearning framework. Experiments on MuST-C speech translation benchmark and further analysis show that our method effectively alleviates the cross-modal representation discrepancy, and achieves significant improvements over a strong baseline on eight translation directions. * indicates corresponding authors. † Work was done while at ByteDance AI Lab. Part of joint project between ICT/CAS and ByteDance AI Lab. Work was done when QF was a member of the joint project. Code and models are publicly available at https:// github.com/ictnlp/STEMM .