Wav2Vec Meets Conformer: A Novel Hybrid Approach for Multilingual Deepfake Audio Detection

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@article{1712477,
      author = {Usha Janakiraman, Priyadharshini Ambalavanan, Padmapriya S},
      title = {Wav2Vec Meets Conformer: A Novel Hybrid Approach for Multilingual Deepfake Audio Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {2438-2446},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712477.pdf},
      abstract = {Deepfake audio refers to synthetic speech that closely mimics a person?s voice, posing risks to security and privacy. This paper proposes a hybrid detection framework combining XLS-R, a multilingual speech representation model, with the Conformer architecture, which captures both local and global audio dependencies. XLS-R extracts rich multilingual embeddings, while the Conformer leverages temporal and contextual features to distinguish genuine from AI-generated speech. Evaluation on benchmark datasets demonstrates that the proposed system achieves improved accuracy and robustness across multiple languages and acoustic conditions.},
      keywords = {Conformer, Deepfake Audio, Multilingual Speech Representation, XLS-R},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1712477}
  }