Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling   We propose smoothed max pooling loss and its application to keyword spotting
systems. The proposed approach jointly trains an encoder (to detect keyword
parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The
proposed new loss function allows training a model to detect parts and whole of
a keyword, without strictly depending on frame-level labeling from LVCSR (Large
vocabulary continuous speech recognition), making further optimization
possible. The proposed system outperforms the baseline keyword spotting model
in [1] due to increased optimizability. Further, it can be more easily adapted
for on-device learning applications due to reduced dependency on LVCSR.

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The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol