Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Synthesized Speech Detection Using Convolutional Transformer-Based
  Spectrogram Analysis   Synthesized speech is common today due to the prevalence of virtual
assistants, easy-to-use tools for generating and modifying speech signals, and
remote work practices. Synthesized speech can also be used for nefarious
purposes, including creating a purported speech signal and attributing it to
someone who did not speak the content of the signal. We need methods to detect
if a speech signal is synthesized. In this paper, we analyze speech signals in
the form of spectrograms with a Compact Convolutional Transformer (CCT) for
synthesized speech detection. A CCT utilizes a convolutional layer that
introduces inductive biases and shared weights into a network, allowing a
transformer architecture to perform well with fewer data samples used for
training. The CCT uses an attention mechanism to incorporate information from
all parts of a signal under analysis. Trained on both genuine human voice
signals and synthesized human voice signals, we demonstrate that our CCT
approach successfully differentiates between genuine and synthesized speech
signals.

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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th