A novel approach based on Support Vector Machines for automatic speaker identification
Rania Chakroun, Leila Zouari, Mondher Frikha, Ahmed Ben Hamida · 2015
Over the past decade, the field of automatic speaker recognition has been the subject of extensive research looking for an efficient determination of a person's identity. Despite the essential role played by acoustic characteristics in order to discriminate between speakers. The research of discriminative information about a person remains a major challenge. The main objective of this paper is to present a new approach employing additional information which is dialect detection with a novel parameterization of the speech to improve the task of speaker identification. The superiority of the proposed system has been demonstrated by different kernels function of Support Vector Machines (SVM) with speakers taken from TIMIT database.