A cascaded speech-to-text translation model that feeds five aligned ASR candidates and self-supervised speech units to a translation model matches end-to-end performance on GigaST, with an English-to-Chinese BLEU of 38.1.
Self-Training for End-to-End Speech Translation
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abstract
One of the main challenges for end-to-end speech translation is data scarcity. We leverage pseudo-labels generated from unlabeled audio by a cascade and an end-to-end speech translation model. This provides 8.3 and 5.7 BLEU gains over a strong semi-supervised baseline on the MuST-C English-French and English-German datasets, reaching state-of-the art performance. The effect of the quality of the pseudo-labels is investigated. Our approach is shown to be more effective than simply pre-training the encoder on the speech recognition task. Finally, we demonstrate the effectiveness of self-training by directly generating pseudo-labels with an end-to-end model instead of a cascade model.
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When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation
A cascaded speech-to-text translation model that feeds five aligned ASR candidates and self-supervised speech units to a translation model matches end-to-end performance on GigaST, with an English-to-Chinese BLEU of 38.1.