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ICASSP
2010
IEEE
14 years 9 months ago
Subspace Gaussian Mixture Models for speech recognition
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the...
Daniel Povey, Lukas Burget, Mohit Agarwal, Pinar A...
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ICASSP
2010
IEEE
14 years 9 months ago
Multilingual acoustic modeling for speech recognition based on subspace Gaussian Mixture Models
Although research has previously been done on multilingual speech recognition, it has been found to be very difficult to improve over separately trained systems. The usual approa...
Lukas Burget, Petr Schwarz, Mohit Agarwal, Pinar A...
ICPR
2008
IEEE
15 years 4 months ago
Parameter-based reduction of Gaussian mixture models with a variational-Bayes approach
This paper 1 proposes a technique for simplifying a given Gaussian mixture model, i.e. reformulating the density in a more parcimonious manner, if possible (less Gaussian componen...
Pierrick Bruneau, Marc Gelgon, Fabien Picarougne
MICCAI
2008
Springer
15 years 10 months ago
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
In this paper, a spatially constrained mixture model for the segmentation of MR brain images is presented. The novelty of this work is a new, edge preserving, smoothness prior whic...
Giorgos Sfikas, Christophoros Nikou, Nikolas P. ...
CVPR
2008
IEEE
15 years 11 months ago
Context and observation driven latent variable model for human pose estimation
Current approaches to pose estimation and tracking can be classified into two categories: generative and discriminative. While generative approaches can accurately determine human...
Abhinav Gupta, Trista Chen, Francine Chen, Don Kim...