"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
This work addresses data scarcity in speech translation for researchers and practitioners, though it is incremental as it builds on existing methods with novel supervision.
The paper tackles the problem of limited parallel data in end-to-end speech-to-text translation by proposing the Listen-Understand-Translate (LUT) framework, which uses triple supervision signals to decouple the task, achieving state-of-the-art performance on benchmarks like Librispeech English-French, IWSLT English-German, and TED English-Chinese.
An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to fully utilize signals in a parallel ST corpus? We are inspired by human understanding system which is composed of auditory perception and cognitive processing. In this paper, we propose Listen-Understand-Translate, (LUT), a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task. LUT is able to guide the acoustic encoder to extract as much information from the auditory input. In addition, LUT utilizes a pre-trained BERT model to enforce the upper encoder to produce as much semantic information as possible, without extra data. We perform experiments on a diverse set of speech translation benchmarks, including Librispeech English-French, IWSLT English-German and TED English-Chinese. Our results demonstrate LUT achieves the state-of-the-art performance, outperforming previous methods. The code is available at https://github.com/dqqcasia/st.