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This paper introduces a preprocessing pipeline for keypoints extracted using MediaPipe, aiming to improve pose annotation consistency in sign language datasets. We evaluate its effectiveness using a sign similarity task based on phonological features, without relying on gloss annotations. Similarity is measured using Dynamic Time Warping (DTW) across videos from sign language dictionaries. Although such similarity analyses can support various sign language processing applications - such as lexical search, clustering, and data enrichment - the main contribution of this work is to standardise pose features across heterogeneous sources, including different signers and backgrounds. Experiments on two dictionary datasets show that our pipeline significantly improves similarity measurements, with promising benefits for other sign language processing tasks.

This paper introduces a preprocessing pipeline for keypoints extracted using MediaPipe, aiming to improve pose annotation consistency in sign language datasets. We evaluate its effectiveness using a sign similarity task based on phonological features, without relying on gloss annotations. Similarity is measured using Dynamic Time Warping (DTW) across videos from sign language dictionaries. Although such similarity analyses can support various sign language processing applications - such as lexical search, clustering, and data enrichment - the main contribution of this work is to standardise pose features across heterogeneous sources, including different signers and backgrounds. Experiments on two dictionary datasets show that our pipeline significantly improves similarity measurements, with promising benefits for other sign language processing tasks.

Our goal is to develop an automatic speech recognition (ASR) system for regional languages. To achieve this, we are exploring the specialization or adaptation of Whisper through fine-tunning. In this article, we present our feedbacks on ongoing work in two languages: Basque and Alsatian.

The 17th c. is crucial for the French language, as it sees the creation of a strict orthographic norm that largely persists to this day. Despite its significance, the history of spelling systems remains however an overlooked area in linguistics for two reasons. On the one hand, spelling is made up of microchanges which requires a quantitative approach, and on the other hand, no corpus is available due to the interventions of editors in almost all the texts already available. In this paper, we therefore propose a new corpus allowing such a study, as well as the extraction and analysis tools necessary for our research. By comparing the text extracted with OCR and a version automatically aligned with contemporary French spelling, we extract the variant zones, we categorise these variants, and we study their frequency to study the (ortho)graphic change during the 17th century.

Whether or not several Creole languages which developed during the early modern period can be considered genetic descendants of European languages has been the subject of intense debate. This is in large part due to the absence of evidence of intermediate forms. This work introduces a new open corpus, the Molyé corpus, which combines stereotypical representations of three kinds of language variation in Europe with early attestations of French-based Creole languages across a period of 400 years. It is intended to facilitate future research on the continuity between contact situations in Europe and Creolophone (former) colonies.