Le présent travail se propose de renouveler les traditionnels atlas dialectologiques pour cartographier les variantes de prononciation en français, à travers un site internet. La toile est utilisée non seulement pour collecter des données, mais encore pour disséminer les résultats auprès des chercheurs et du grand public. La méthodologie utilisée, à base de crowdsourcing (ou « production participative »), nous a permis de recueillir des informations auprès de 2500 francophones d’Europe (France, Belgique, Suisse). Une plateforme dynamique à l’interface conviviale a ensuite été développée pour cartographier la prononciation de 70 mots dans les différentes régions des pays concernés (des mots notamment à voyelle moyenne ou dont la consonne finale peut être prononcée ou non). Les options de visualisation par département/canton/province ou par région, combinant plusieurs traits de prononciation et ensembles de mots, sous forme de pastilles colorées, de hachures, etc. sont présentées dans cet article. On peut ainsi observer immédiatement un /E/ plus fermé (ainsi qu’un /O/ plus ouvert) dans le Nord-Pas-de-Calais et le sud de la France, pour des mots comme parfait ou rose, un /Œ/ plus fermé en Suisse pour un mot comme gueule, par exemple.

Text and speech corpora for training a tale telling robot have been designed, recorded and annotated. The aim of these corpora is to study expressive storytelling behaviour, and to help in designing expressive prosodic and co-verbal variations for the artificial storyteller). A set of 89 children tales in French serves as a basis for this work. The tales annotation principles and scheme are described, together with the corpus description in terms of coverage and inter-annotator agreement. Automatic analysis of a new tale with the help of this corpus and machine learning is discussed. Metrics for evaluation of automatic annotation methods are discussed. A speech corpus of about 1 hour, with 12 tales has been recorded and aligned and annotated. This corpus is used for predicting expressive prosody in children tales, above the level of the sentence.

A Hungarian multimodal spontaneous expressive speech corpus was recorded following the methodology of a similar French corpus. The method relied on a Wizard of Oz scenario-based induction of varying affective states. The subjects were interacting with a supposedly voice-recognition driven computer application using simple command words. Audio and video signals were captured for the 7 recorded subjects. After the experiment, the subjects watched the video recording of their session and labelled the recorded corpus themselves, freely describing the evolution of their affective states. The obtained labels were later classified into one of the following broad emotional categories: satisfaction, dislike, stress, or other. A listening test was performed by 25 naïve listeners in order to validate the category labels originating from the self-labelling. For 52 of the 149 stimuli, listeners’ judgements of the emotional content were in agreement with the labels. The result of the listening test was compared with an earlier test validating a part of the French corpus. While the French test had a higher success ratio, validating the labels of 79 tested stimuli, out of the 193, the stimuli validated by the two tests can form the basis of cross linguistic comparison experiments.