Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate Detection of Wake Word Start and End Using a CNN   Small footprint embedded devices require keyword spotters (KWS) with small
model size and detection latency for enabling voice assistants. Such a keyword
is often referred to as \textit{wake word} as it is used to wake up voice
assistant enabled devices. Together with wake word detection, accurate
estimation of wake word endpoints (start and end) is an important task of KWS.
In this paper, we propose two new methods for detecting the endpoints of wake
words in neural KWS that use single-stage word-level neural networks. Our
results show that the new techniques give superior accuracy for detecting wake
words' endpoints of up to 50 msec standard error versus human annotations, on
par with the conventional Acoustic Model plus HMM forced alignment. To our
knowledge, this is the first study of wake word endpoints detection methods for
single-stage neural KWS.

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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc