Abstract. Uncertainty quantification is necessary in wind turbine design due to the random nature of environmental inputs, through which uncertainty structural loads and response under specific situations can be quantified. Specifically, turbulence (described by standard deviation longitudinal speed over a 10 min time duration) has significant impact on extreme fatigue envelope turbine. The par...

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 a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP faci...

We develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients’ population is captured via a hierarchical latent class model; the proposed algorithm aims to “discover” the number of latent classes in the patients’ population, and t...

For many applied problems in the context of clustering via mixture models, the estimates of the component means and covariance matrices can be affected by observations that are atypical of the components in the mixture model being fitted. In this paper, we consider for Gaussian mixtures a robust estimation procedure using multiresolution kd-trees. The method provides a fast EM-based approach to...