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TIP
2008
128views more  TIP 2008»
13 years 7 months ago
Blind Separation of Superimposed Shifted Images Using Parameterized Joint Diagonalization
We consider the blind separation of source images from linear mixtures thereof, involving different relative spatial shifts of the sources in each mixture. Such mixtures can be cau...
E. Be'ery, Arie Yeredor
ICIP
2001
IEEE
14 years 8 months ago
EM algorithms of Gaussian mixture model and hidden Markov model
The HMM (Hidden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The GMM (Gaussian Mixture Mo...
Guorong Xuan, Wei Zhang, Peiqi Chai
NN
2002
Springer
136views Neural Networks» more  NN 2002»
13 years 6 months ago
Bayesian model search for mixture models based on optimizing variational bounds
When learning a mixture model, we suffer from the local optima and model structure determination problems. In this paper, we present a method for simultaneously solving these prob...
Naonori Ueda, Zoubin Ghahramani
ICASSP
2010
IEEE
13 years 7 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...
CSL
2011
Springer
13 years 2 months ago
The subspace Gaussian mixture model - A structured model for speech recognition
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gauss...
Daniel Povey, Lukas Burget, Mohit Agarwal, Pinar A...