Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation

Abstract

End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, making synthetic data augmentation by backtranslation or knowledge distillation a necessary ingredient of end-to-end training. In this paper, we present a novel approach to data augmentation that leverages audio alignments, linguistic properties, and translation. First, we augment a transcription by sampling from a suffix memory that stores text and audio data. Second, we translate the augmented transcript. Finally, we recombine concatenated audio segments and the generated translation.Besides training an MT-system, we only use basic off-the-shelf components without finetuning. While having similar resource demands as knowledge distillation, adding our method delivers consistent improvements of up to 0.9 and 1.1 BLEU points on five language pairs on CoVoST 2 and on two language pairs on Europarl-ST, respectively.

Efficient Speech Translation with Pre-trained Models

“…The lack of end-to-end training data is another obstacle to the end-to-end ST system. Recent work address this obstacle by leveraging the available data resource using multi-task learning [7,8,9], transfer learning [10,11] and generating synthetic data [12,13,14] techniques. This work extends this idea with a novel loss function to efficiently use the available data.…”