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<Paper uid="N04-2010">
  <Title>Speaker Recognition with Mixtures of Gaussians with Sparse Regression Matrices</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> When estimating a mixture of Gaussians there are usually two choices for the covariance type of each Gaussian component. Either diagonal or full covariance. Imposing a structure though may be restrictive and lead to degraded performance and/or increased computations. In this work, several criteria to estimate the structure of regression matrices of a mixture of Gaussians are introduced and evaluated. Most of the criteria attempt to estimate a discriminative structure, which is suited for classification tasks. Results are reported on the 1996 NIST speaker recognition task and performance is compared with structural EM, a well-known, non-discriminative, structure-finding algorithm.</Paragraph>
  </Section>
class="xml-element"></Paper>