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Offre Stage M2 : Sound Zones adaptatives par apprentissage automatique sur un large jeu de données
Adaptive Sound Zones through machine learning on a large dataset Level: Master 2 Supervisors:
Sparse Autoencoders Make Audio Foundation Models more Explainable
Seminar from Théo Mariotte, researcher at LIUM Date : 03/11/2025 Time : 10h30 Place : IC2, Boardroom Speaker : Théo Mariotte Sparse Autoencoders Make Audio Foundation Models more Explainable Abstract : Audio pretrained models are widely employed to solve various tasks in speech processing, sound event detection, or music information retrieval. […]
Double séminaire Deepfake Detection
Seminars from Hoan My Tran, PhD Candidate at IRISA, and Heydi Mendez, researcher at CENATAV, Cuba Date : 29/09/2025 Time : 10h00 Place : IC2, Boardroom Speakers : Hoan My Tran and Heydi Mendez Summary : The rapid advances in speech synthesis and voice conversion technologies have fueled the rise of audio deepfakes, […]
LST-day
LST day The LST team’s summer day is being held on 16 June. On this occasion, the researchers – both young and more experienced – present their research themes. It is also the occasion for our guest, Edwin Simonet, to present the Apside company. After a general presentation and a reminder of the year’s […]
Données pseudo-étiquetées de kurde central vers l’anglais pour la traduction de la parole
Corpus: Central Kurdish to English Pseudo-Labeled Data for Speech Translation (Données pseudo-étiquetées de kurde central vers l’anglais pour la traduction de la parole)Licence: CC BY 4.0 licenseAuthor(s): Mohammad MohammadaminiDescription In this repository, you will find large-scale pseudo-labeled data, including Central Kurdish audio translated into English. This dataset contains 1.7 million samples, equivalent to 3,000 hours […]
Machine Learning models explainability for text and audio classification
Seminar from Norbert Tsopze, Professor at Université Yaounde 1 Date: 13/05/2025 Time: 11h00 Place: IC2, Boardroom Speaker: Norbert Tsopze Machine Learning models explainability for text and audio classification Abstract : The interpretability of the Machine Learning models outcomes to the end user is one of the most important properties which favors […]
Towards a Smarter Homophone Correction Tool: A Case Study in Khmer Writing
Seminar from Seanghort BORN, PhD student at LIUM-TEL Date: 7/04/2025 Time: 11h00 Place: IC2, Boardroom Speaker: Seanghort BORN Towards a Smarter Homophone Correction Tool: A Case Study in Khmer Writing Homophone errors are a common challenge in written communication, affecting both high-resource languages like English and low-resourced languages such as Khmer. […]
Thèse en détection de deepfakes
SAVID – Speaker and Audiovisual Interpretable Deepfake Detection Supervisors: Marie Tahon (director) and Aghilas Sini (co-supervisor) at LIUM and Arnaud Delhay (co-director) and Damien Lolive (co-supervisor) at l’IRISA Hosting teams : LST-LIUM et EXPRESSION-IRISA Location : Le Mans Beginning : October 2025 Contact : aghilas.sini(at)univ-lemans.fr, arnaud.delhay(at)irisa.fr Description : The proliferation of text-to-speech and facial […]
Using large ASR models for training lightweight models in low-resource and computation-limited languages
Seminar from Aran Mohammadamini, Post-doc fellow at LIUM Date: 24/03/2025 Time: 11h00 Place: IC2, Boardroom Speaker: Aran Mohammadamini Using large ASR models for training lightweight models in low-resource and computation-limited languages Low-resource languages often suffer not only from a lack of language resources but also from limited computational resources. Recent multilingual […]
Advances in measuring the interpretability of speaker representation spaces
Seminar from Félix Saget, PhD student at LIUM Date: 10/03/2025 Time: 11h00 Place: IC2, Boardroom Speaker: Félix Saget Advances in measuring the interpretability of speaker representation spaces Features extracted by speaker representation models have proven to be potent and versatile, yielding respectable performance in various speaker-related tasks. However, a human user […]