WST-Graph: Topology-Preserving Wavelet Scattering Front-End for Speech Deepfake Detection

Abstract: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 while using approximately 60% fewer trainable parameters and show clear gains on selected out-of-domain benchmarks. These results underscore the value of preserving parent-child relations within the carrier-modulation topology when constructing a compact, physically grounded interface for graph-based speech deepfake detection. Code will be released at this https URL.

Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)

arXiv:2609.29372 [eess.AS]

(or arXiv:2609.29372v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2609.29372 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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