ALIZE 3.0-Open Source Toolkit for State-of-the-Art Speaker Recognition

Anthony Larcher (1) , Jean-François Bonastre (2) , Benoît Fauve (3) , Kong Aik Lee (4) , Christophe Levy (2) , Haizhou Li (4) , John Mason (3) , Jean-Yves Parfait

1 LIUM - Laboratoire d'Informatique de l'Université du Mans
2 LIA - Laboratoire Informatique d'Avignon
3 Swansea University
4 Institute for Infocomm Research - I²R [Singapore]

ALIZE is an open-source platform for speaker recognition. The ALIZE library implements a low-level statistical engine based on the well-known Gaussian mixture modelling. The toolkit includes a set of high level tools dedicated to speaker recognition based on the latest developments in speaker recognition such as Joint Factor Analysis, Support Vector Machine, i-vector modelling and Probabilistic Linear Discriminant Analysis. Since 2005, the performance of ALIZE has been demonstrated in series of Speaker Recognition Evaluations (SREs) conducted by NIST and has been used by many participants in the last NIST-SRE 2012. This paper presents the latest version of the corpus and performance on the NIST-SRE 2010 extended task.

Mots clés: speaker recognition; open-source platform; i-vector

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Anthony Larcher, Jean-François Bonastre, Benoît Fauve, Kong Aik Lee, Christophe Levy, et al.. ALIZE 3.0-Open Source Toolkit for State-of-the-Art Speaker Recognition. Annual Conference of the International Speech Communication Association, Aug 2013, Lyon, France. ⟨hal-01927586⟩