Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Deep Learning Techniques and Inferential Speech Statistics for AI
  Synthesised Speech Recognition   The recent developments in technology have re-warded us with amazing audio
synthesis models like TACOTRON and WAVENETS. On the other side, it poses
greater threats such as speech clones and deep fakes, that may go undetected.
To tackle these alarming situations, there is an urgent need to propose models
that can help discriminate a synthesized speech from an actual human speech and
also identify the source of such a synthesis. Here, we propose a model based on
Convolutional Neural Network (CNN) and Bidirectional Recurrent Neural Network
(BiRNN) that helps to achieve both the aforementioned objectives. The temporal
dependencies present in AI synthesized speech are exploited using Bidirectional
RNN and CNN. The model outperforms the state-of-the-art approaches by
classifying the AI synthesized audio from real human speech with an error rate
of 1.9% and detecting the underlying architecture with an accuracy of 97%.

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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin