Tor André Myrvoll

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Transformation-based model adaptation techniques like maximum likelihood linear regression (MLLR) rely on an accurate selection of the number of transformations for a given amount of adaptation data. If too many transformations are used, the transformation parameters may be poorly estimated, can overfit the adaptation data, and offer poor generalization. On(More)
We present an algorithm for clustering multivariate normal distributions based upon the symmetric, Kullback-Leibler divergence. Optimal mean vector and covariance matrix of the centroid normal distribution are derived and a set of Riccati matrix equations is used to find the optimal covariance matrix. The solutions are found iteratively by alternating the(More)
We present an optimal clustering algorithm for grouping multivariate normal distributions into clusters using the divergence, a symmetric, information-theoretic distortion measure based on the Kullback-Liebler distance. Optimal solutions for normal distributions are shown to he obtained by solving a set of Riccati matrix equations and the optimal centroids(More)
In this paper we introduce an approach to transformation based model adaptation techniques. Previously published schemes like MLLR define a set of affine transformations to be applied on clusters of model parameters. Although it has been shown that this approach can yield good results when adaptation data is scarce, an inherent problem needs to be(More)
In our previous work (2003), we investigated a new approach to robust speech recognition. An exact procedure was developed to filter noisy cepstral coefficients in the mean-square-error sense, and it was shown that this method outperformed the well known vector Taylor series (VTS) approach, which in turn is based on linear approximations to the non-linear(More)
In this paper, a new approach for model adaptation, extended maximum a posterior linear regression (EMAPLR), is described and studied. EMAPLR is an extension of maximum a posterior linear regression (MAPLR) for transform based model adaptation. The proposed approach has a close form solution under the elliptic symmetric matrix variate priors, and it is(More)
In this paper we present an approach that makes use of both Bayesian predictive classification (BPC) and parallel model combination (PMC) to achieve increased robustness towards noise. PMC provides a method for finding parameter estimates for speech corrupted by noise, while BPC is a method that compensates for uncertainty of parameter estimates. Thus,(More)
In this work we investigate the use of a greedy training algorithm for use with the dual penalized logistic regression machine (dPLRM), and our target application is detection of broad class phonetic features. The use of a greedy training algorithm is meant to alleviate the infeasible memory and computational demands that arises during the learning phase(More)
We propose an algorithm for optimal clustering and nonuniform allocation of Gaussian Kernels in scalar (feature) dimension to compress complex, Gaussian mixture-based, continuous density HMMs into computationally efficient, small footprint models. The symmetric Kullback-Leibler divergence (KLD) is used as the universal distortion measure and it is minimized(More)
In this paper, we focus on communication from airplanes to Air traffic Control (ATC) towers at airports, employing multiple antennas at the ATC. The whole air-to-ground communication system can then be modeled as a MIMO MAC channel with mobile users. The main aim is to demonstrate that multiuser MIMO systems can be useful for achieving spectrally efficient(More)