@inproceedings{macaire-etal-2024-multimodal-french,
title = "A Multimodal {F}rench Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation",
author = "Macaire, C{\'e}cile and
Dion, Chlo{\'e} and
Arrigo, Jordan and
Lemaire, Claire and
Esperan{\c{c}}a-Rodier, Emmanuelle and
Lecouteux, Benjamin and
Schwab, Didier",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.76",
pages = "839--849",
abstract = "The automatic translation of spoken language into pictogram units can facilitate communication involving individuals with language impairments. However, there is no established translation formalism or publicly available datasets for training end-to-end speech translation systems. This paper introduces the first aligned speech, text, and pictogram translation dataset ever created in any language. We provide a French dataset that contains 230 hours of speech resources. We create a rule-based pictogram grammar with a restricted vocabulary and include a discussion of the strategic decisions involved. It takes advantage of an in-depth linguistic study of resources taken from the ARASAAC website. We validate these rules through multiple post-editing phases by expert annotators. The constructed dataset is then used to experiment with a Speech-to-Pictogram cascade model, which employs state-of-the-art Automatic Speech Recognition models. The dataset is freely available under a non-commercial licence. This marks a starting point to conduct research into the automatic translation of speech into pictogram units.",
}
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%0 Conference Proceedings
%T A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation
%A Macaire, Cécile
%A Dion, Chloé
%A Arrigo, Jordan
%A Lemaire, Claire
%A Esperança-Rodier, Emmanuelle
%A Lecouteux, Benjamin
%A Schwab, Didier
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F macaire-etal-2024-multimodal-french
%X The automatic translation of spoken language into pictogram units can facilitate communication involving individuals with language impairments. However, there is no established translation formalism or publicly available datasets for training end-to-end speech translation systems. This paper introduces the first aligned speech, text, and pictogram translation dataset ever created in any language. We provide a French dataset that contains 230 hours of speech resources. We create a rule-based pictogram grammar with a restricted vocabulary and include a discussion of the strategic decisions involved. It takes advantage of an in-depth linguistic study of resources taken from the ARASAAC website. We validate these rules through multiple post-editing phases by expert annotators. The constructed dataset is then used to experiment with a Speech-to-Pictogram cascade model, which employs state-of-the-art Automatic Speech Recognition models. The dataset is freely available under a non-commercial licence. This marks a starting point to conduct research into the automatic translation of speech into pictogram units.
%U https://aclanthology.org/2024.lrec-main.76
%P 839-849
Markdown (Informal)
[A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation](https://aclanthology.org/2024.lrec-main.76) (Macaire et al., LREC-COLING 2024)
ACL
- Cécile Macaire, Chloé Dion, Jordan Arrigo, Claire Lemaire, Emmanuelle Esperança-Rodier, Benjamin Lecouteux, and Didier Schwab. 2024. A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 839–849, Torino, Italia. ELRA and ICCL.