Paper Info
Title
Gender independent discriminative speaker recognition in i-vector space
Abstract
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.
Year
DOI
Venue
2012
10.1109/ICASSP.2012.6288885
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
gender issues,learning (artificial intelligence),speaker recognition,support vector machines,GI SVM,discriminative training,gender dependent features,gender independent discriminative speaker recognition,gender independent system,i-vector pairs,i-vector space,log-likelihood ratio score function,pairwise SVM,speaker recognition systems,Discriminative Training,I-vector,PLDA,SVM,Speaker Recognition
I vector,Pairwise comparison,Feature vector,Pattern recognition,Computer science,Support vector machine,Speech recognition,NIST,Speaker recognition,Artificial intelligence,Score,Discriminative model
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4673-0044-5
978-1-4673-0044-5
9
PageRank 
References 
Authors
0.51
7
6
Name
Order
Citations
PageRank
Sandro Cumani120018.81
Ondřej Glembek285264.75
Niko Brümmer359544.01
Edward de Villiers41358.81
Pietro Laface537860.68
de Villiers, E.690.51