Speaker recognition using a binary representation and specificities models

Abstract

State of the Art speaker recognition methods are mainly based on GMM/UBM based supervector paradigm. Recently, a simple representation of speech based on local binary decision taken on each acoustic frame have been proposed, allowing to represent a speech excerpt as a binary matrix. This article is based on a similar approach. A new temporal block representation of the binary transformed data as well as three simple algorithms to obtain an efficient similarity measure are proposed. The experimental results show a better robustness of the proposed approach and a similar or better overall performance over classical approaches. © 2012 Springer-Verlag.