Speaker verification using kernel-based binary classifiers with binary operation derived features
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages
1660-1664
Date Issued
2014
Author(s)
Abstract
In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the 'within-speaker' group or the 'between-speaker' group. To solve the problem, we employ various binary operations to retain the basic relationship between any pair of i-vectors to form a single vector for training the discriminative models. This study also investigates the correlation of achievable performances with the number of training pairs and the various combinations of basic binary operations, using the SVM and DNN binary classifiers. The experiments are conducted on the male portion of the core task in the NIST 2005 Speaker Recognition Evaluation (SRE), and the results are competitive or even better, in terms of normalized decision cost function (minDCF) and equal error rate (EER), while compared to other non-probabilistic based models, such as the conventional speaker SVMs and the LDA-based cosine distance scoring. © 2014 IEEE.
Subjects
DNN; i-vector; speaker verification; SVM
Other Subjects
Classification (of information); Signal processing; Support vector machines; Vectors; Achievable performance; Binary classification problems; Cosine distance scoring; DNN; I vectors; Speaker recognition evaluations; Speaker verification; SVM; Speech recognition
Type
conference paper