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.

Pretrained Language Models (PLMs) are the de facto backbone of most state-of-the-art NLP systems. In this paper, we introduce a family of domain-specific pretrained PLMs for French, focusing on three important domains: transcribed speech, medicine, and law. We use a transformer architecture based on efficient methods (LinFormer) to maximise their utility, since these domains often involve processing long documents. We evaluate and compare our models to state-of-the-art models on a diverse set of tasks and datasets, some of which are introduced in this paper. We gather the datasets into a new French-language evaluation benchmark for these three domains. We also compare various training configurations: continued pretraining, pretraining from scratch, as well as single- and multi-domain pretraining. Extensive domain-specific experiments show that it is possible to attain competitive downstream performance even when pre-training with the approximative LinFormer attention mechanism. For full reproducibility, we release the models and pretraining data, as well as contributed datasets.

PROPICTO is a project funded by the French National Research Agency and the Swiss National Science Foundation, that aims at creating Speech-to-Pictograph translation systems, with a special focus on French as an input language. By developing such technologies, we intend to enhance communication access for non-French speaking patients and people with cognitive impairments.

Nous présentons un ensemble de trois interfaces web pour la création de données en pictogrammes dans le cadre du projet ANR Propicto. Chacune a un objectif précis : annoter des données textuelles en pictogrammes ARASAAC, créer un vocabulaire en pictogrammes, et post-éditer des phrases annotées en pictogrammes. Bien que nécessaire pour des outils de traduction automatique vers les unités pictographiques, actuellement, presque aucune ressource annotée n’existe. Cet article présente les spécificités de ces plateformes web (disponibles en ligne gratuitement) et leur utilité.

Nous présentons Voice2Picto, un système de traduction permettant, à partir de l’oral, de proposer une séquence de pictogrammes correspondants. S’appuyant sur des technologies du traitement automatique du langage naturel, l’outil a deux objectifs : améliorer l’accès à la communication pour (1) les personnes allophones dans un contexte d’urgence médicale, et (2) pour les personnes avec des difficultés de parole. Il permettra aux personnes des services hospitaliers, et aux familles de véhiculer un message en pictogrammes facilement compréhensible auprès de personnes ne pouvant communiquer via les canaux traditionnels de communication (parole, gestes, langue des signes). Dans cet article, nous décrivons l’architecture du système de Voice2Picto et les pistes futures. L’application est en open-source via un dépôt Git : https://github.com/macairececile/Voice2Picto.

La Communication Alternative et Augmentée (CAA) prend une place importante chez les personnes en situation de handicap ainsi que leurs proches à cause de la difficulté de son utilisation. Pour réduire ce poids, l’utilisation d’outils de traduction de la parole en pictogrammes est pertinente. De plus, ils peuvent être d’une grande aide pour l’accessibilité communicative dans le milieu hospitalier. Dans cet article, nous présentons un projet de recherche visant à développer un système de traduction de la parole vers des pictogrammes. Il met en jeu une chaîne de traitement comportant plusieurs axes relevant du traitement automatique des langues et de la parole, tels que la reconnaissance automatique de la parole, l’analyse syntaxique, la simplification de texte et la traduction automatique vers les pictogrammes. Nous présentons les difficultés liées à chacun de ces axes ainsi que, pour certains, les pistes de résolution.

This is a report on results obtained in the development of speech recognition tools intended to support linguistic documentation efforts. The test case is an extensive fieldwork corpus of Japhug, an endangered language of the Trans-Himalayan (Sino-Tibetan) family. The goal is to reduce the transcription workload of field linguists. The method used is a deep learning approach based on the language-specific tuning of a generic pre-trained representation model, XLS-R, using a Transformer architecture. We note difficulties in implementation, in terms of learning stability. But this approach brings significant improvements nonetheless. The quality of phonemic transcription is improved over earlier experiments; and most significantly, the new approach allows for reaching the stage of automatic word recognition. Subjective evaluation of the tool by the author of the training data confirms the usefulness of this approach.

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.