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Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection

Abstract : End-to-end approaches to anti-spoofing, especially those which operate directly upon the raw signal, are starting to be competitive with their more traditional counterparts. Until recently, all such approaches consider only the learning of network parameters; the network architecture is still hand crafted. This too, however, can also be learned. Described in this paper is our attempt to learn automatically the network architecture of a speech deepfake and spoofing detection solution, while jointly optimising other network components and parameters, such as the first convolutional layer which operates on raw signal inputs. The resulting raw differentiable architecture search system delivers a tandem detection cost function score of 0.0517 for the ASVspoof 2019 logical access database, a result which is among the best single-system results reported to date.
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https://hal.archives-ouvertes.fr/hal-03555727
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Submitted on : Thursday, February 3, 2022 - 5:39:16 PM
Last modification on : Friday, February 4, 2022 - 5:49:23 PM

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Wanying Ge, Jose Patino, Massimiliano Todisco, Nicholas Evans. Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection. ASVSPOOF 2021, Automatic Speaker Verification and Spoofing Countermeasures Challenge, Sep 2021, Online, France. pp.22-28, ⟨10.21437/ASVSPOOF.2021-4⟩. ⟨hal-03555727⟩

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