The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 200...
In this paper we present a novel approach for estimating featurespace maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maxim...
Arnab Ghoshal, Daniel Povey, Mohit Agarwal, Pinar ...
In this paper, we present an adaptive model for dynamically deforming hyper-elastic rods. In contrast to existing approaches, adaptively introduced control points are not governed...
Over the years, the focus in noise robust speech recognition has shifted from noise robust features to model based techniques such as parallel model combination and uncertainty de...
Kris Demuynck, Xueru Zhang, Dirk Van Compernolle, ...
Abstract--In this paper, we introduce a novel approach for improved nonlinear system identification in the short-time Fourier transform (STFT) domain. We first derive explicit repr...