Papers by José Fonollosa
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations (2023.findings-emnlp)
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| Challenge: | End-to-end Speech Translation models are limited by a data bottleneck . end-to end models can address several shortcomings of cascaded models . |
| Approach: | They propose a data augmentation strategy to augment sentence-level datasets by using an Audio Segmentation system to re-segment the speech of each document with different length constraints. |
| Outcome: | The proposed method achieves state-of-the-art results in MuST-C and in mTEDx. |
Pushing the Limits of Zero-shot End-to-End Speech Translation (2024.findings-acl)
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| Challenge: | Existing approaches to end-to-end Speech Translation (ST) systems require limited data, which can cause data scarcity and performance degradation. |
| Approach: | They propose a method for zero-shot ST that bridges the modality gap without any paired ST data. |
| Outcome: | The proposed method bridges the modality gap without any paired ST data on a speech encoder and on MT models. |