Corpus: Topic Segmentation (FrNewsLink)URL: https://hal.archives-ouvertes.fr/hal-01741177FrNewsLink package allows to adress several applicative tasks in the domain of topic and titling segmentation. It is compososed of a set of resources from a varied corpus of French Broadcast News (BN) and press articles. Due to broadcasting rights, this package does not contain videos or audios files. The corpus […]
Soutenance de thèse, Gaël LE LAN Titre : Analyse en locuteurs de collections de documents multimédia. Composition du jury : Guillaume GRAVIER, DR CNRS, IRISA, Président, Claude BARRAS, MCF HDR, Paris XI, Rapporteur, Jean-François BONASTRE, PR, Université d’Avignon, Rapporteur Corrine FREDOUILLE, MCF HDR, Université d’Avignon, Sylvain MEIGNIER, Le Mans Université, Directeur de thèse, Delphine CHARLET, […]
PhD defence, Sahar GHANNAY Title : A study of continuous word representations applied to ASR error detection. Composition of the jury : Présidente : Martine Adda-Decker, Reviewers : Sophie Rosset, Frédéric Béchet, Examiners : Benoit Favre, Benjamin Lecouteux, Supervisor : Yannick Estève Co-supervisor : Nathalie Camelin Abstract : This thesis concerns a study of continuous […]
Deep neural networks for oral and written language processingStarting: 04/09/2017PhD Student: Antoine CaubrièreAdvisor(s): Yannick Estève (LIUM, LST)Co-advisor(s): Antoine Laurent (LIUM, LST) & Emmanuel Morin (LS2N)Funding: RAPACE ProjectThe aim of this thesis is to develop a named entity recognition system in an audio stream that will rely solely on a deep neural network. Until now, this […]
Towards a hybrid approach for Arabic Sentiment AnalysisStarting: 03/10/2016PhD Student: Amira BarhoumiAdvisor(s): Yannick Estève (LIUM, LST)Co-advisor(s): Nathalie Camelin (LIUM, LST) & Lamia Hadrich Belguith (MIRACL, Tunisie)Funding: Agreement “Cotutelle Convention” (LIUM, LST) & (MIRACL, Tunisie)Sentiment analysis is a growing field of research and has been subject of numerous studies. This thesis aims at designing a hybrid […]
Software: NMTPYTORCHLicence: MIT LicenseGitHub: https://github.com/lium-lst/nmtpytorch/URL: https://arxiv.org/abs/1706.00457Author(s): Ozan CaglayanMercedes García MartínezAdrien BardetWalid AransaFethi BougaresLoïc BarraultThis is the PyTorch fork of nmtpy, a sequence-to-sequence framework which was originally a fork of dl4mt-tutorial.
Software: SIDEKITLicence: LGPLGitHub: https://git-lium.univ-lemans.fr/Larcher/sidekitURL: https://projets-lium.univ-lemans.fr/sidekit/Author(s): Anthony LarcherKong Aik LeeSylvain Meignier Welcome to SIDEKIT documentation! SIDEKIT is an open source package for Speaker and Language recognition. The aim of SIDEKIT is to provide an educational and efficient toolkit for speaker/language recognition including the whole chain of treatment that goes from the audio data to the analysis […]
Software: SIDEKIT for diarization (s4d)Licence: LGPLGitHub: https://git-lium.univ-lemans.fr/Meignier/s4dURL: https://projets-lium.univ-lemans.fr/s4d/Author(s): Pierre-Alexandre BrouxFlorent DesnousAnthony LarcherSylvain Meignier Welcome to SIDEKIT for diarization documentation! SIDEKIT for diarization (s4d as short name) is an open source package extension of SIDEKIT for Speaker diarization . The aim of S4D is to provide an educational and efficient toolkit for speaker diarization including the […]
Thematic segmentation of automatic transcriptions and enrichment of educational documents in a lecture contextStarting: 23/01/2017PhD Student: Salima MdhaffarAdvisor(s): Yannick Estève (LIUM, LST)Co-advisor(s): Antoine Laurent (LIUM, LST), Nicolas Hernandez (LS2N), Solen Quiniou (LS2N)Funding: ANR PASTEL ProjectThis thesis is a part of the PASTEL project (Performing Automated Speech Transcription for Enhancing Learning), which aims to explore the […]
Variable context modeling for speaker recognitionStarting: 03/10/2016PhD Student: Florent DesnousAdvisor(s): Sylvain Meignier (LIUM – LST)Co-advisor(s): Anthony Larcher (LIUM – LST)Funding: Contrat DoctoralThe aim of this thesis is to develop variable context speaker models (scalable) that integrate phonetic information produced by the speaker. These models will be learned on a significant amount of enrollment data (> […]
Français
