Gender independent discriminative speaker recognition in i-vector space

Speaker recognition systems attain their best accuracy when trained with gender dependent features and tested with known gender trials. In real applications, however, gender labels are often not given. In this work we illustrate the design of a system that does not make use of the gender labels both in training and in test, i.e. a completely Gender Independent (GI) system. It relies on discriminative training, where the trials are i-vector pairs, and the discrimination is between the hypothesis that the pair of feature vectors in the trial belong to the same speaker or to different speakers. We demonstrate that this pairwise discriminative training can be interpreted as a procedure that estimates the parameters of the best (second order) approximation of the log-likelihood ratio score function, and that a pairwise SVM can be used for training a gender independent system. Our results show that a pairwise GI SVM, saving memory and execution time, achieves on the last NIST evaluations state-of-the-art performance, comparable to a Gender Dependent(GD) system.