ON-TRAC consortium systems for the IWSLT 2023 dialectal and low-resource speech translation tasks
Résumé
This paper describes the ON-TRAC consortium speech translation systems developed for IWSLT 2023 evaluation campaign. Overall, we participated in three speech translation tracks featured in the low-resource and dialect speech translation shared tasks, namely; i) spoken Tamasheq to written French, ii) spoken Pashto to written French, and iii) spoken Tunisian to written English. All our primary submissions are based on the end-to-end speech-to-text neural architecture using a pre-trained SAMU-XLSR model as a speech encoder and an mbart model as a decoder. The SAMU-XLSR model is built from the XLS-R 128 in order to generate language agnostic sentence-level embeddings. This building is driven by the LaBSE model trained on a multilingual text dataset. This architecture allows us to improve the input speech representations and achieve significant improvements compared to conventional endto-end speech translation systems.
Domaines
Informatique et langage [cs.CL]| Origine | Fichiers produits par l'(les) auteur(s) |
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