The Speaker Recognition community that participates in NIST evaluations has concentrated on designing genderand channel-conditioned systems. In the real word, this conditioning is not feasible. Our main purpose in this work is to propose a mixture of Probabilistic Linear Discriminant Analysis models (PLDA) as a solution for making systems independent of speaker gender. In order to show the effe...

Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model lead...

In this paper we propose to estimate the parameters of the probabilistic linear discriminant analysis (PLDA) in textindependent i-vector speaker verification framework using a nonparametric form rather than maximum likelihood estimation (MLE) obtained by an EM algorithm. In this approach the between-speaker covariance matrix that represents global information about the speaker variability is re...

Short duration speaker verification is a challenging problem partly due to utterance duration mismatch. This paper proposes a novel method that modifies the standard Gaussian probabilistic linear discriminant analysis (G-PLDA) to use two separate generative models for i-vectors from long and short utterances which are jointly trained. The proposed twin model G-PLDA employs distinct models for i...

This paper analyses the short utterance probabilistic linear discriminant analysis (PLDA) speaker verification with utterance partitioning and short utterance variance (SUV) modelling approaches. Experimental studies have found that instead of using single long-utterance as enrolment data, if long enrolledutterance is partitioned into multiple short utterances and average of short utterance i-v...

We propose a communication-e cient distributed estimation method for sparse linear discriminant analysis (LDA) in the high dimensional regime. Our method distributes the data of size N into m machines, and estimates a local sparse LDA estimator on each machine using the data subset of size N/m. After the distributed estimation, our method aggregates the debiased local estimators from m machines...

A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classical Fisher linear discriminant analysis (LDA) and useful, for example, in supervised segmentation tasks in which high-dimensional feature vector describes the local structure of the image. In general, the main idea of t...

The linear discriminant analysis (LDA) is one of the most traditional linear dimensionality reduction methods. This paper incorporates the inter-class relationships as relevance weights into the estimation of the overall within-class scatter matrix in order to improve the performance of the basic LDA method and some of its improved variants. We demonstrate that in some specific situations the s...

We develop, in the context of discriminant analysis, a general approach to the design of neural architectures. It consists in building a neural net ‘around’ a statistical model family; larger networks, made up of such elementary networks, are then constructed. It is shown that, on the one hand, the statistical modeling approach provides a systematic way to obtaining good approximations in the n...

Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysi...