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Alzheimer's disease (AD) is currently the most common form of dementia affecting the elderly, and its occurrence rate is only expected to increase over the next several decades. Though there is a vast array of knowledge about individual molecules and genetics involved with the disease, there is no clear understanding of the mechanism of pathogenesis. To help better understand the disease process,...
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 total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define the subspace. This style of acoustic model allows for a much more compact representation and gives better results...
In this paper we present a novel approach for estimating feature-space maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maximizing the likelihood function by repeated line search in the direction of the gradient. We do this in a pre-transformed parameter space such that an approximation to the expected Hessian is proportional to the unit matrix...
MIMO system capacity depends on the assumptions about what channel information is known at the transmitters and receivers; i.e., channel state information (CSI) and/or channel distribution information (CDI). We investigate three simple power allocation strategies for improving the maximum possible signaling rate (ergodic capacity) in MIMO systems with complete CSI at the receiver and periodically...
This paper presents a model for machine-aided human translation (MAHT) that integrates source language text and target language acoustic information to produce the text translation of source language document. It is evaluated on a scenario where a human translator dictates a first draft target language translation of a source language document. Information obtained from the source language document,...
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