C.H. Morimoto, D. Koons, et al.
Image and Vision Computing
This paper proposes a new covariance modeling technique for Gaussian Mixture Models. Specifically the inverse covariance (precision) matrix of each Gaussian is expanded in a rank-1 basis i.e., ∑j-1 = Pj = ∑k=1D λkjakakT, λkj ∈ ℝ, ak ∈ ℝd. A generalized EM algorithm is proposed to obtain maximum likelihood parameter estimates for the basis set {akakT} k=1D and the expansion coefficients {λ kj}. This model, called the Extended Maximum Likelihood Linear Transform (EMLLT) model, is extremely flexible: by varying the number of basis elements from D = d to D = d(d + 1)/2 one gradually moves from a Maximum Likelihood Linear Transform (MLLT) model to a full-covariance model. Experimental results on two speech recognition tasks show that the EMLLT model can give relative gains of up to 35% in the word error rate over a standard diagonal covariance model, 30% over a standard MLLT model.
C.H. Morimoto, D. Koons, et al.
Image and Vision Computing
Jessica He, David Piorkowski, et al.
CHIWORK 2023
Cen Rao, Alexei Gritai, et al.
ICCV 2003
Kun Wang, Juwei Shi, et al.
PACT 2011