Automatic Classification of Phonation Types in Spontaneous Speech: Towards a New Workflow for the Characterization of Speakers' Voice Quality. - Anaïs Chanclu; Imen Ben Amor; Cédric Gendrot; Emmanuel Ferragne; Jean-François Bonastre (Interspeech, 2021)

BA-LR: Binary-Attribute-based Likelihood Ratio estimation for forensic voice comparison. - Imen Ben Amor; Jean-François Bonastre (IWBF, 2022)

Describing the phonetics in the underlying speech attributes for deep and interpretable speaker recognition. - Imen Ben Amor; Jean-François Bonastre; Benjamin O'Brien; Pierre-Michel Bousquet (INTERSPEECH, 2023)

Extraction of interpretable and shared speaker-specific speech attributes through binary auto-encoder. - Imen Ben Amor; Jean-François Bonastre; Salima Mdhaffar (INTERSPEECH, 2024)

Forensic speaker recognition with BA-LR: calibration and evaluation on a forensically realistic database. - Imen Ben Amor; Jean-François Bonastre; David van der Vloed (Odyssey, 2024)

Deep modeling based on voice attributes for explainable speaker recognition : application in the forensic domain. (Modélisation profonde basée sur la notion d'attributs de voix pour la reconnaissance du locuteur explicable : application au domaine criminalistique). - Imen Ben Amor (2024)