Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders

TL;DR

This paper introduces a novel stacked acoustic-and-textual encoding approach for speech translation that effectively integrates pre-trained models, leading to state-of-the-art results and surpassing traditional cascaded systems.

Contribution

It proposes a new SATE method combining acoustic and textual encoding, along with an adaptor and knowledge distillation, to improve end-to-end speech translation performance.

Findings

Achieves state-of-the-art BLEU scores of 18.3 and 25.2 on LibriSpeech En-Fr and MuST-C En-De.

First end-to-end ST system with comparable or better performance than cascaded systems.

Effectively incorporates pre-trained models into speech translation encoders.

Abstract

Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for translation, whereas MT encoders are not designed to deal with long but locally attentive acoustic sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. Our encoder begins with processing the acoustic sequence as usual, but later behaves more like an MT encoder for a global representation of the input sequence. In this way, it is straightforward to incorporate the pre-trained models into the system. Also, we develop an adaptor module to alleviate the representation inconsistency between the pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge distillation method to preserve the pre-training knowledge. Experimental results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.