AES Europe 2026

In this paper, we analyze two main factors of Bonafide
Resource (BR) or AI-based Generator (AG) which affect the
performance; 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); 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;
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;
conduct cross-dataset evaluation on different benchmark
datasets. The cross-dataset evaluation results prove that
the balance of Bonafide Resources (BR); AI-based
Generators (AG) is the key factor to train; achieve a
general Deepfake Speech Detection (DSD) model.