WST-Graph: Topology-Preserving Wavelet Scattering Front-End for Speech Deepfake Detection
The acoustic front-end determines which forensic cues a speech deepfake detector can exploit. The wavelet scattering transform (WST) provides stable multiscale coefficients with explicit coordinates, yet direct flattening obscures the parent relation between paths. We introduce WST-Graph, reconstructing these paths as a sparse modulation-carrier grid for an AASIST graph backend. Modulation-level normalization and length-aware adaptive local attention pooling produce fixed relative-time representations while retaining the acoustic axes before learned adaptation. This yields a waveform-to-graph interface with a fixed, parameter-free WST. Our configurations remain competitive with AASIST whi...
| Authors | Kwok-Ho Ng, Tingting Song, Bingwen Feng et al. |
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| Primary category | eess.AS |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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