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Deep Learning Serves Voice Cloning: How Vulnerable Are Automatic Speaker Verification Systems to Spoofing Trials?

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10244762" target="_blank" >RIV/61989100:27240/20:10244762 - isvavai.cz</a>

  • Alternative codes found

    RIV/61989100:27740/20:10244762

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/8999436" target="_blank" >https://ieeexplore.ieee.org/document/8999436</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/MCOM.001.1900396" target="_blank" >10.1109/MCOM.001.1900396</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Deep Learning Serves Voice Cloning: How Vulnerable Are Automatic Speaker Verification Systems to Spoofing Trials?

  • Original language description

    This article verifies the reliability of automatic speaker verification (ASV) systems on new synthesis methods based on deep neural networks. ASV systems are widely used and applied regarding secure and effective biometric authentication. On the other hand, the rapid deployment of ASV systems contributes to the increased attention of attackers with newer and more sophisticated spoofing methods. Until recently, speech synthesis of the reference speaker did not seriously compromise the latest ASV systems. This situation is changing with the deployment of deep neural networks into the synthesis process. Projects including WaveNet, Deep Voice, Voice Loop, and many others generate very natural and high-quality speech that may clone voice identity. We are slowly approaching an era where we will not be able to recognize a genuine voice from a synthesized one. Therefore, it is necessary to define the robustness of current ASV systems to new methods of voice cloning. In this article, well-known SVM and GMM as well as new CNN-based ASVs are applied and subjected to synthesized speech from Tacotron 2 with the WaveNet TTS system. The results of this work confirm our concerns regarding the reliability of ASV systems against synthesized speech.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20203 - Telecommunications

Result continuities

  • Project

    <a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE COMMUNICATIONS MAGAZINE

  • ISSN

    0163-6804

  • e-ISSN

  • Volume of the periodical

    58

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    6

  • Pages from-to

    100-105

  • UT code for WoS article

    000521968600018

  • EID of the result in the Scopus database