Robust speaker verification on short utterances remains a key consideration when deploying automatic speaker recognition, as many real world applications often have access to only limited duration speech data. This paper explores how the recent technologies focused around total variability modeling behave when training and testing utterance lengths are reduced. Results are presented which provi...

Neurological studies are often concerned with identifying abnormalities in brain structure affecting asymmetry between left and right hemispheres. This paper presents techniques which allow measurement and characterisation of differences between neuroanatomic structures due to variation in both shape and receptor distribution. This provides a potentially powerful tool for identifying subtle pat...

not available at time of publication. Eugene Agichtein Emory University Mathematics & Computer Science Department [email protected]

In this paper, the result of applying iterative univariate resultant constructions to multivariate polynomials is analyzed. We consider the input polynomials as generic polynomials of a given degree and exhibit explicit decompositions into irreducible factors of several constructions involving two times iterated univariate resultants and discriminants over the integer universal ring of coe cien...

Purpose – In this study the researchers attempt to identify factors that could influence an individual’s attitude toward ethical behavior in the information systems (IS) environment and compare them to the findings of an earlier study to determine any changes. Design/methodology/approach – A sample of university students is used to assess environmental influences (societal, belief system, perso...

This paper presents a novel methodology whose task is to deal with the face classification problem. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropybased measure, that contain the...

|This paper describes the automatic selection of features from an image training set using the theories of multi-dimensional linear discriminant analysis and the associated optimal linear projection. We demonstrate the eeectiveness of these Most Discriminating Features for view-based class retrieval from a large database of widely varying real-world objects presented as \well-framed" views, and...

In this paper we discuss some issues which arise when applying classical data analysis techniques to interval data, focusing on the notions of dispersion, association and linear combinations of interval variables. We present some methods that have been proposed for analysing this kind of data, namely for clustering, discriminant analysis, linear regression and interval time series analysis.

This paper provides a unifying view of three discriminant linear feature extraction methods: Linear Discriminant Analysis, Heteroscedastic Discriminant Analysis and Maximization of Mutual Information. We propose a model-independent reformulation of the criteria related to these three methods that stresses their similarities and elucidates their differences. Based on assumptions for the probabil...