Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Initialization Scheme for Meeting Separation with Spatial Mixture
  Models   Spatial mixture model (SMM) supported acoustic beamforming has been
extensively used for the separation of simultaneously active speakers. However,
it has hardly been considered for the separation of meeting data, that are
characterized by long recordings and only partially overlapping speech. In this
contribution, we show that the fact that often only a single speaker is active
can be utilized for a clever initialization of an SMM that employs time-varying
class priors. In experiments on LibriCSS we show that the proposed
initialization scheme achieves a significantly lower Word Error Rate (WER) on a
downstream speech recognition task than a random initialization of the class
probabilities by drawing from a Dirichlet distribution. With the only
requirement that the number of speakers has to be known, we obtain a WER of 5.9
%, which is comparable to the best reported WER on this data set. Furthermore,
the estimated speaker activity from the mixture model serves as a diarization
based on spatial information.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
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The categories of this paper are: econ