Researchers Propose Audio Spoofing Detection System for English and Arabic

A research paper published today describes a new framework for detecting audio spoofing attacks, including deepfakes, that target automatic speaker verification systems. The framework, named Phonetic-DeepKANet, was developed to improve generalization against unknown attacks and to extend coverage to Arabic, a language often overlooked in such research due to limited datasets.

The approach uses a dual-modality design that pairs deep features extracted by TransRawNet with multi-view acoustic-phonetic representations. These combined features are then processed by a Kolmogorov Arnold Network classifier. The paper states that this combination allows reliable detection across voice conversion, text-to-speech, partial spoofing and codec-related distortions.

Researchers introduced an Arabic audio spoofing dataset as part of the work to support evaluation in that language. The system was tested on the ASVspoof-2019 LA, ASVspoof-2021 LA, ASVspoof-2021 DF, partial spoofing and the newly created Arabic dataset.

Experiments included algorithm-wise and cross-corpora evaluations.

The framework recorded a minimum tandem detection cost function of 0.09 on the ASVspoof-2019 LA dataset and 0.14 on the ASVspoof-2021 LA dataset. On the Arabic dataset it achieved an equal error rate of 8.06 percent. According to the paper, the method produced the best result for logical access attacks among 41 entries in the ASVspoof-2021 challenge and the third-best equal error rate of 17.55 percent on the deepfake track among 33 participants.

The authors reported that the system demonstrates improved generalization when facing codec compressions, channel variations and encoding artifacts compared with baseline models. Audio spoofing attacks have been used to breach security in speaker verification systems, resulting in data breaches and financial scams.

Existing countermeasures have shown limited generalization to unknown spoofing attacks, including deepfakes. The introduction of an Arabic dataset aims to reduce that gap for underrepresented languages. The research received support from a grant under the Cybersecurity Research and Innovation Pioneers Grants Initiative provided by the National Cybersecurity Authority in Saudi Arabia.

The paper was received on December 14, 2025, accepted on April 30, 2026, and published on May 9, 2026.