Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic context window composition for distant speech recognition   Distant speech recognition is being revolutionized by deep learning, that has
contributed to significantly outperform previous HMM-GMM systems. A key aspect
behind the rapid rise and success of DNNs is their ability to better manage
large time contexts. With this regard, asymmetric context windows that embed
more past than future frames have been recently used with feed-forward neural
networks. This context configuration turns out to be useful not only to address
low-latency speech recognition, but also to boost the recognition performance
under reverberant conditions. This paper investigates on the mechanisms
occurring inside DNNs, which lead to an effective application of asymmetric
contexts.In particular, we propose a novel method for automatic context window
composition based on a gradient analysis. The experiments, performed with
different acoustic environments, features, DNN architectures, microphone
settings, and recognition tasks show that our simple and efficient strategy
leads to a less redundant frame configuration, which makes DNN training more
effective in reverberant scenarios.

29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat