Self-Supervised Representations Improve End-to-End Speech Translation

• URL: http://arxiv.org/abs/2006.12124v2
• Date: Sun, 25 Oct 2020 03:31:15 GMT
• Title: Self-Supervised Representations Improve End-to-End Speech Translation
• Authors: Anne Wu, Changhan Wang, Juan Pino, Jiatao Gu
• Abstract summary: We show that self-supervised pre-trained features can consistently improve the translation performance. Cross-lingual transfer allows to extend to a variety of languages without or with little tuning.
• Score: 57.641761472372814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end speech-to-text translation can provide a simpler and smaller system but is facing the challenge of data scarcity. Pre-training methods can leverage unlabeled data and have been shown to be effective on data-scarce settings. In this work, we explore whether self-supervised pre-trained speech representations can benefit the speech translation task in both high- and low-resource settings, whether they can transfer well to other languages, and whether they can be effectively combined with other common methods that help improve low-resource end-to-end speech translation such as using a pre-trained high-resource speech recognition system. We demonstrate that self-supervised pre-trained features can consistently improve the translation performance, and cross-lingual transfer allows to extend to a variety of languages without or with little tuning.

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