Nonnative speech recognition is becoming more and more important as many speech applications are deployed world wide. Meanwhile, due to the large population of nonnative speakers, speaker adaptation remains the most practical way for providing high performance speech services. Subspace Gaussian Mixture Model (SGMM) has recently been shown to yield superior performance on various native speech r...

In developing speech recognition based services for any task domain, it is necessary to account for the support of an increasing number of languages over the life of the service. This paper considers a small vocabulary speech recognition task in multiple Indian languages. To configure a multi-lingual system in this task domain, an experimental study is presented using data from two linguistical...

We propose an algorithm for nonparametric estimation for finite mixtures of multivariate random vectors that is not, but that strongly resembles, a true EM algorithm. The vectors are assumed to have independent coordinates conditional upon knowing which mixture component from which they come, but otherwise their density functions are completely unspecified. Sometimes, the density functions may ...

Abundance estimates from animal point-count surveys require accurate estimates of detection probabilities. The standard model for estimating detection from removalsampled point-count surveys assumes that organisms at a survey site are detected at a constant rate; however, this assumption is often not justified. We consider a class of N-mixture models that allows for detection heterogeneity over...

Detection of sparse signals arises in a wide range of modern scientific studies. The focus so far has been mainly on Gaussian mixture models. In this paper, we consider the detection problem under a general sparse mixture model and obtain an explicit expression for the detection boundary. It is shown that the fundamental limits of detection is governed by the behavior of the log-likelihood rati...

This paper is concerned with estimating preference functionals for choice under risk from the choice behaviour of individuals. We note that there is heterogeneity in behaviour between individuals and within individuals. By ‘heterogeneity between individuals’ we mean that people are different, in terms of both their preference functionals and their parameters for these functionals. By ‘heterogen...

ion levels of words document partitioning abstraction levels (a) (b)

ABSTRACT This paper is concerned with improving the attitude estimation accuracy by implementing an adaptive Gaussian sum filter where the a posteriori density function is approximated by a sum of Gaussian density functions. Compared to the traditional Gaussian sum filter, this adaptive approach utilizes the Fokker-Planck-Kolmogorov residual minimization to update the weights associated with di...

When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations require...