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ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation
- Publication Year :
- 2023
-
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.<br />Comment: NeurIPS 2023, Poster
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2305.14838
- Document Type :
- Working Paper