Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prototypical Metric Transfer Learning for Continuous Speech Keyword
  Spotting With Limited Training Data   Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords
in recorded conversations, when a small number of instances of keywords are
available in training data. Unlike the more common Keyword Spotting, where an
algorithm needs to detect lone keywords or short phrases like "Alexa",
"Cortana", "Hi Alexa!", "Whatsup Octavia?" etc. in speech, CSKS needs to filter
out embedded words from a continuous flow of speech, ie. spot "Anna" and
"github" in "I know a developer named Anna who can look into this github
issue." Apart from the issue of limited training data availability, CSKS is an
extremely imbalanced classification problem. We address the limitations of
simple keyword spotting baselines for both aforementioned challenges by using a
novel combination of loss functions (Prototypical networks' loss and metric
loss) and transfer learning. Our method improves F1 score by over 10%.

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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: q-bio
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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: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc