QAMO: Quality-aware Multi-centroid One-class Learning For Speech Deepfake Detection

Recent work shows that one-class learning can detect unseen deepfake attacks by modeling a compact distribution of bona fide speech around a single centroid. However, the single-centroid assumption can oversimplify the bona fide speech representation and overlook useful cues, such as speech quality, which reflects the naturalness of the speech. Speech quality can be easily obtained using existing speech quality assessment models that estimate it through Mean Opinion Score. In this paper, we propose QAMO: Quality-Aware Multi-Centroid One-Class Learning for speech deepfake detection. QAMO extends conventional one-class learning by introducing multiple quality-aware centroids. In QAMO, each centroid is optimized to represent a distinct speech quality subspaces, enabling better modeling of intra-class variability in bona fide speech. In addition, QAMO supports a multi-centroid ensemble scoring strategy, which improves decision thresholding and reduces the need for quality labels during inference. With two centroids to represent high- and low-quality speech, our proposed QAMO achieves an equal error rate of 5.09% in In-the-Wild dataset, outperforming previous one-class and quality-aware systems.

Key Contributions

• Quality-Aware Multi-Centroid One-Class Learning (QAMO) framework that extends single-centroid one-class learning by assigning each centroid to a distinct MOS-derived speech quality subspace
• Multi-centroid ensemble scoring strategy that aggregates distances across all quality-aware centroids, eliminating the need for quality labels at inference time
• Achieves 5.09% EER on the In-the-Wild dataset, outperforming prior single-centroid one-class and quality-aware speech deepfake detection systems

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