Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic
  Speech Recognition   Selecting in-domain data from a large pool of diverse and out-of-domain data
is a non-trivial problem. In most cases simply using all of the available data
will lead to sub-optimal and in some cases even worse performance compared to
carefully selecting a matching set. This is true even for data-inefficient
neural models. Acoustic Latent Dirichlet Allocation (aLDA) is shown to be
useful in a variety of speech technology related tasks, including domain
adaptation of acoustic models for automatic speech recognition and entity
labeling for information retrieval. In this paper we propose to use aLDA as a
data similarity criterion in a data selection framework. Given a large pool of
out-of-domain and potentially mismatched data, the task is to select the
best-matching training data to a set of representative utterances sampled from
a target domain. Our target data consists of around 32 hours of meeting data
(both far-field and close-talk) and the pool contains 2k hours of meeting,
talks, voice search, dictation, command-and-control, audio books, lectures,
generic media and telephony speech data. The proposed technique for training
data selection, significantly outperforms random selection, posterior-based
selection as well as using all of the available data.

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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th