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Jean-Yves Antoine

Researcher at François Rabelais University

Publications -  121
Citations -  1001

Jean-Yves Antoine is an academic researcher from François Rabelais University. The author has contributed to research in topics: Spoken language & Parsing. The author has an hindex of 15, co-authored 118 publications receiving 942 citations. Previous affiliations of Jean-Yves Antoine include University of Southern Brittany & European University of Brittany.

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Proceedings Article

The EPAC corpus: manual and automatic annotations of conversational speech in French broadcast news

TL;DR: This paper presents the EPAC corpus which is composed by a set of 100 hours of conversational speech manually transcribed and by the outputs of automatic tools applied on the entire French ESTER 1 audio corpus: this concerns about 1700 hours of audio recordings from radiophonic shows.
Proceedings ArticleDOI

Weighted Krippendorff's alpha is a more reliable metrics for multi-coders ordinal annotations: experimental studies on emotion, opinion and coreference annotation

TL;DR: An experimental study with four measures (Cohen's κ, Scott's π, binary and weighted Krippendorff ' s α) on three tasks: emotion, opinion and coreference annotation suggests that weighted α is the most reliable metrics for such an annotation scheme.
Proceedings Article

The French MEDIA/EVALDA Project: the Evaluation of the Understanding Capability of Spoken Language Dialogue Systems.

TL;DR: This paper will present and report on the progress of the EVALDA/MEDIA project, focusing on the recording and annotating protocol of the reference dialogue corpus, to design and test an evaluation methodology to compare and diagnose the context-dependent and independent understanding capability of spoken language dialogue systems.
Journal ArticleDOI

Sibylle, An Assistive Communication System Adapting to the Context and Its User

TL;DR: The latest version of Sibylle, an AAC system that permits persons who have severe physical disabilities to enter text with any computer application, as well as to compose messages to be read out through speech synthesis, is described.
Proceedings Article

Methods to Integrate a Language Model with Semantic Information for a Word Prediction Component

TL;DR: This paper explored the predictive powers of Latent Semantic Analysis (LSA), a method that has been shown to provide reliable information on long-distance semantic dependencies between words in a context, and presented several methods that integrate LSA-based information with a standard language model: a semantic cache, partial re-ranking, and different forms of interpolation.