Cross-Lingual Transfer Learning for Speech Translation

• URL: http://arxiv.org/abs/2407.01130v1
• Date: Mon, 1 Jul 2024 09:51:48 GMT
• Title: Cross-Lingual Transfer Learning for Speech Translation
• Authors: Rao Ma, Yassir Fathullah, Mengjie Qian, Siyuan Tang, Mark Gales, Kate Knill,
• Abstract summary: Zero-shot cross-lingual transfer has been demonstrated on a range of NLP tasks. We explore whether speech-based models exhibit the same transfer capability.
• Score: 7.802021866251242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There has been increasing interest in building multilingual foundation models for NLP and speech research. Zero-shot cross-lingual transfer has been demonstrated on a range of NLP tasks where a model fine-tuned on task-specific data in one language yields performance gains in other languages. Here, we explore whether speech-based models exhibit the same transfer capability. Using Whisper as an example of a multilingual speech foundation model, we examine the utterance representation generated by the speech encoder. Despite some language-sensitive information being preserved in the audio embedding, words from different languages are mapped to a similar semantic space, as evidenced by a high recall rate in a speech-to-speech retrieval task. Leveraging this shared embedding space, zero-shot cross-lingual transfer is demonstrated in speech translation. When the Whisper model is fine-tuned solely on English-to-Chinese translation data, performance improvements are observed for input utterances in other languages. Additionally, experiments on low-resource languages show that Whisper can perform speech translation for utterances from languages unseen during pre-training by utilizing cross-lingual representations.

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