Nous utilisons des données de terrain en créole haïtien, récoltées il y a $40$ ans sur cassettes puis numérisées, pour entraîner un modèle natif d’apprentissage auto-supervisé (SSL) de la parole (Wav2Vec2) en haïtien. Nous utilisons une approche de pré-entraînement continu (CPT) sur des modèles SSL pré-entraînés de deux langues étrangères : la langue lexificatrice – le français – et une langue non apparentée – l’anglais. Nous comparons les performances de ces trois modèles SSL, et de deux autres modèles SSL étrangers directement affinés, sur une tâche de reconnaissance de la parole. Nos résultats montrent que le modèle le plus performant est celui qui a été entraîné en utilisant une approche CPT sur la langue lexificatrice, suivi par le modèle natif. Nous concluons que l’approche de ”mobilisation des archives” préconisée par (Bird, 2020) est une voie prometteuse pour concevoir des technologies vocales pour de nouvelles langues.

In recent times, there has been a growing number of research studies focused on addressing the challenges posed by low-resource languages and the transcription bottleneck phenomenon. This phenomenon has driven the development of speech recognition methods to transcribe regional and Indigenous languages automatically. Although there is much talk about bridging the gap between speech technologies and field linguistics, there is a lack of documented efficient communication between NLP experts and documentary linguists. The models created for low-resource languages often remain within the confines of computer science departments, while documentary linguistics remain attached to traditional transcription workflows. This paper presents the early stage of a collaboration between NLP experts and field linguists, resulting in the successful transcription of Kréyòl Gwadloupéyen using speech recognition technology.

We investigate the exploitation of self-supervised models for two Creole languages with few resources: Gwadloupéyen and Morisien. Automatic language processing tools are almost non-existent for these two languages. We propose to use about one hour of annotated data to design an automatic speech recognition system for each language. We evaluate how much data is needed to obtain a query-by-example system that is usable by linguists. Moreover, our experiments show that multilingual self-supervised models are not necessarily the most efficient for Creole languages.

This paper reports a critical analysis of the ISO TimeML standard, in the light of several experiences of temporal annotation that were conducted on spoken French. It shows that the norm suffers from weaknesses that should be corrected to fit a larger variety of needs inNLP and in corpus linguistics. We present our proposition of some improvements of the norm before it will be revised by the ISO Committee in 2017. These modifications concern mainly (1) Enrichments of well identified features of the norm: temporal function of TIMEX time expressions, additional types for TLINK temporal relations; (2) Deeper modifications concerning the units or features annotated: clarification between time and tense for EVENT units, coherence of representation between temporal signals (the SIGNAL unit) and TIMEX modifiers (the MOD feature); (3) A recommendation to perform temporal annotation on top of a syntactic (rather than lexical) layer (temporal annotation on a treebank).

This article presents ANCOR_Centre, a French coreference corpus, available under the Creative Commons Licence. With a size of around 500,000 words, the corpus is large enough to serve the needs of data-driven approaches in NLP and represents one of the largest coreference resources currently available. The corpus focuses exclusively on spoken language, it aims at representing a certain variety of spoken genders. ANCOR_Centre includes anaphora as well as coreference relations which involve nominal and pronominal mentions. The paper describes into details the annotation scheme and the reliability measures computed on the resource.