Speaker identification System (SIS) and automatic speaker verification (ASV) like voice-based systems are essential in industries like finance and healthcare for confirming identities using unique speech patterns. However, these systems can be cheated by spoofing attacks. A new method called Enhanced Adversarial Spectro-Phonetic Learning (EASPL) is introduced in this research. EASPL uses deep learning with adversarial training and a mix of spectral and phonetic features. This approach helps the system learn to recognize fake voices better by exposing it to synthetic adversarial examples during training. EASPL is tested on the Voice Conversion Challenge (VCC) 2022 dataset and showed great results with an accuracy of 99.68% and an equal error rate (EER) of 0.011, making it effective and reliable in spotting spoofing attacks while being efficient. Additionally, the model's robustness against varied spoofing techniques demonstrates potential for real-world applications.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: RKR, TT;
Methodology: RKR, TT;
Software: RKR;
Data Curation: TT;
Writing- Original Draft Preparation: RKR, TT;
Visualization: TT;
Supervision: TT;
Validation: RKR, TT;
Writing- Reviewing and Editing: RKR, TT;
Writing- Original Draft: TT; All authors reviewed the results and approved the final version of the manuscript.
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Cite this article
Rahul Kumar Ravichandran and Thamaraimanalan T, "Enhanced Adversarial Spectro Phonetic Learning for Robust Voice Spoof Detection Using VCC 2022 Dataset", Journal of Future Networks and Communications, vol.1, no.1, pp. 041-050, January 2025. doi: XXXX/XXXX/JFNC202501005.
