Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Voice Activity Detection for Ultrasound-based Silent Speech Interfaces
  using Convolutional Neural Networks   Voice Activity Detection (VAD) is not easy task when the input audio signal
is noisy, and it is even more complicated when the input is not even an audio
recording. This is the case with Silent Speech Interfaces (SSI) where we record
the movement of the articulatory organs during speech, and we aim to
reconstruct the speech signal from this recording. Our SSI system synthesizes
speech from ultrasonic videos of the tongue movement, and the quality of the
resulting speech signals are evaluated by metrics such as the mean squared
error loss function of the underlying neural network and the Mel-Cepstral
Distortion (MCD) of the reconstructed speech compared to the original. Here, we
first demonstrate that the amount of silence in the training data can have an
influence both on the MCD evaluation metric and on the performance of the
neural network model. Then, we train a convolutional neural network classifier
to separate silent and speech-containing ultrasound tongue images, using a
conventional VAD algorithm to create the training labels from the corresponding
speech signal. In the experiments our ultrasound-based speech/silence separator
achieved a classification accuracy of about 85\% and an AUC score around 86\%.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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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: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: physics