ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation

• URL: http://arxiv.org/abs/2305.14838v2
• Date: Sat, 14 Oct 2023 08:47:43 GMT
• Title: ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation
• Authors: Chenyang Le, Yao Qian, Long Zhou, Shujie Liu, Yanmin Qian, Michael Zeng, Xuedong Huang
• Abstract summary: We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks.
• Score: 79.66359274050885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Joint speech-language training is challenging due to the large demand for training data and GPU consumption, as well as the modality gap between speech and language. We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models and optimized data-efficiently for spoken language tasks. Particularly, we propose to incorporate cross-modality learning into transfer learning and conduct them simultaneously for downstream tasks in a multi-task learning manner. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks, achieving a new state-of-the-art average BLEU score of 31.5 on the multilingual speech to English text translation task for 21 languages, as measured on the public CoVoST2 evaluation set.

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