Semi-supervised speaker clustering refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. In the form of an independent training set, the prior knowledge helps us learn a speaker-discriminative feature transformation, a universal speaker prior model, and a discriminative speaker subspace, or equivalently a speaker-discriminative di...

This work explores the speaker verification using fixed phrase short utterances. A novel speaker verification system using Gaussian posteriorgrams is proposed in which the posteriorgram vectors are computed from speaker specific Gaussian mixture model (GMM). The enrollment utterances for each of the target speakers are labeled with GMM trained on the corresponding speaker’s data. The test trial...

In the framework of a Bayesian classifier based on mixtures of gaussians, we address the problem of non-frontal face verification (when only a single (frontal) training image is available) by extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of Maximum Likelihood Linear Regression (MLLR), as w...

This paper presents a speaker clustering framework by iteratively performing two stages: a discriminative feature space is obtained given a cluster label set, and the cluster label set is updated using a clustering algorithm given the feature space. In the iterations of two stages, the cluster labels may be different from the true labels, and thus the obtained feature space based on the labels ...

This paper studies the hypothesis that the acoustic-toarticulatory mapping is non-unique, statistically. The distributions of the acoustic and articulatory spaces are obtained by fitting the data into a Gaussian Mixture Model. The kurtosis is used to measure the non-Gaussianity of the distributions and the Bhattacharya distance is used to find the difference between distributions of the acousti...

Most automatic speech recognition (ASR) systems use Hidden Markov model (HMM) with a diagonal-covariance Gaussian mixture model for the state-conditional probability density function. The diagonal-covariance Gaussian mixture can model discrete sources of variability like speaker variations, gender variations, or local dialect, but can not model continuous types of variability that account for c...

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...

Recent developments in system identification have brought attention to regularized kernel-based methods. This type of approach has been proven to compare favorably with classic parametric methods. However, current formulations are not robust with respect to outliers. In this paper, we introduce a novel method to robustify kernel-based system identification methods. To this end, we model the out...

Evaluating the similarity between two Probability Distribution Functions (PDF) is very important in various research problems. This paper proposes a new metric that computes the distance between two PDF's of mixture type directly from their parameters. It is posed as a linear programming problem and its theoretical properties and performance are analyzed, experimented, and compared with existin...

The existence of large image datasets such as the set of photos on the world wide web make it possible to build powerful generic models for low-level image attributes like color using simple histogram learning techniques. We describe the construction of color models for skin and non-skin classes from a dataset of nearly 1 billion labelled pixels. These classes exhibit a surprising degree of sep...