Frédéric Bimbot
European Union
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Featured researches published by Frédéric Bimbot.
international conference on acoustics, speech, and signal processing | 2002
Mathieu Ben; Raphaël Blouet; Frédéric Bimbot
In this paper, we propose a new score normalization technique in Automatic Speaker Verification (ASV): the D-Norm. The main advantage of this score normalization is that it does not need any additional speech data nor external speaker population, as opposed to the state-of-the-art approaches. The D-Norm is based on the use of Kullback-Leibler (KL) distances in an ASV context. In a first step. we estimate the KL distances with a Monte-Carlo method and we experimentally show that they are correlated with the verification scores. In a second step, we use this correlation to implement a score normalization procedure, the D-Norm. We analyse its performance and we compare it to that of a conventional normalization, the Z-Norm. The results show that performance of the D-Norm is comparable to that of the Z-Norm. We then conclude about the results we obtain and we discuss the applications of this work.
Speaker Classification II | 2007
Sacha Krstulovic; Frédéric Bimbot; Olivier Boëffard; Delphine Charlet; Dominique Fohr; Odile Mella
In the context of the Neologos French speech database creation project, a general methodology was defined for the selection of representative speaker recordings. The selection aims at providing a good coverage in terms of speaker variability while limiting the number of recorded speakers. This is intended to make the resulting database both more adapted to the development of recently proposed multi-model methods and less expensive to collect. The presented methodology proposes a selection process based on the optimization of a quality criterion defined in a variety of speaker similarity modeling frameworks. The selection can be achieved with respect to a unique similarity criterion, using classical clustering methods such as Hierarchical or K-Medians clustering, or it can combine several speaker similarity criteria, thanks to a newly developed clustering method called Focal Speakers Selection. In this framework, four different speaker similarity criteria are tested, and three different speaker clustering algorithms are compared. Results pertaining to the collection of the Neologos database are also discussed.
IEEE Workshop on Automatic Advanced Technologies | 1999
B. Nedic; Guillaume Gravier; Jamal Kharroubi; Gérard Chollet; D. Petrovska-Delacretaz; Geoffrey Durou; Frédéric Bimbot; Raphaël Blouet; Mouhamadou Seck; J.-F Bonastre; Corinne Fredouille; Teva Merlin; Ivan Magrin-Chagnolleau; Stéphane Pigeon; Patrick Verlinde; J. Cernocky
Archive | 2008
Simon Arberet; Rémi Gribonval; Frédéric Bimbot
Audio Engineering Society Conference: 53rd International Conference: Semantic Audio | 2014
Frédéric Bimbot; Gabriel Sargent; Emmanuel Deruty; Corentin Guichaoua; Emmanuel Vincent
MIREX - ISMIR 2010 | 2010
Gabriel Sargent; Frédéric Bimbot; Emmanuel Vincent
Journées d'Informatique Musicale 2010 | 2010
Gabriel Sargent; Frédéric Bimbot; Emmanuel Vincent
Odyssey | 2004
Mathieu Ben; Frédéric Bimbot; Guillaume Gravier
The Music Information Retrieval Evaluation eXchange (MIREX), ISMIR 2012 | 2012
Gabriel Sargent; Frédéric Bimbot; Emmanuel Vincent
Archive | 2012
Emmanuel Deruty; Frédéric Bimbot; Brigitte Van Wymeersch