Split and Conquer Partial Deepfake Speech
Partial deepfake speech detection requires identifying manipulated regions that may occur within short temporal portions of an otherwise bona fide utterance, making the task particularly challenging for conventional utterance-level classifiers. We propose a split-and-conquer framework that decomposes the problem into two stages: boundary detection and segment-level classification. A dedicated boundary detector first identifies temporal transition points, allowing the audio signal to be divided into segments that are expected to contain acoustically consistent content. Each resulting segment is then evaluated independently to determine whether it corresponds to bona fide or fake speech. Th...
| Authors | Inbal Rimon, Oren Gal, Haim Permuter |
|---|---|
| Primary category | cs.SD |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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