ConWST: Non-native Multi-source Knowledge Distillation for Low Resource Speech Translation
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-9247-5_10
Reference46 articles.
1. Weiss, R.J., Chorowski, J., Jaitly, N., Wu, Y., Chen, Z.: Sequence-to-sequence models can directly translate foreign speech. arXiv preprint arXiv:1703.08581 (2017)
2. B’erard, A., Besacier, L., Kocabiyikoglu, A.C., Pietquin, O.: End-to-end automatic speech translation of audiobooks. In: Proceedings of ICASSP (2018)
3. Tang, Y., Pino, J., Wang, C., Ma, X., Genzel, D.: A general multitask learning framework to leverage text data for speech to text tasks. In: Proceedings of ICASSP (2021)
4. Bansal, S., Kamper, H., Livescu, K., Lopez, A., Goldwater, S.: Pre-training on high-resource speech recognition improves low-resource speech-to-text translation. In: Proceedings of NAACL (2019)
5. Stoian, M.C., Bansal, S., Goldwater, S.: Analyzing asr pre-training for low-resource speech-to-text translation. In: Proceedings of ICASSP (2020)
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