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arXiv · 2603.27557

A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators

Lam Pham·Khoi Vu·Dat Tran·David Fischinger·Alexander Schindler·Martin Boyer·Ian McLoughlin
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Abstract

In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generality of a Deepfake Speech Detection (DSD) model. To this end, we first propose a deep-learning based model, referred to as the baseline. Then, we conducted experiments on the baseline by which we indicate how Bonafide Resource (BR) and AI-based Generator (AG) factors affect the threshold score used to detect fake or bonafide input audio in the inference process. Given the experimental results, a dataset, which re-uses public Deepfake Speech Detection (DSD) datasets and shows a balance between Bonafide Resource (BR) or AI-based Generator (AG), is proposed. We then train various deep-learning based models on the proposed dataset and conduct cross-dataset evaluation on different benchmark datasets. The cross-dataset evaluation results prove that the balance of Bonafide Resources (BR) and AI-based Generators (AG) is the key factor to train and achieve a general Deepfake Speech Detection (DSD) model.

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Lam Pham, Khoi Vu, Dat Tran, David Fischinger, Alexander Schindler, Martin Boyer, Ian McLoughlin. 2026-03-29. A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators. https://arxiv.org/abs/2603.27557

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