Open-Set Source Tracing as Compositional Factors via Structured Prototypes

• URL: http://arxiv.org/abs/2607.03134v1
• Date: Fri, 03 Jul 2026 09:23:36 GMT
• Title: Open-Set Source Tracing as Compositional Factors via Structured Prototypes
• Abstract summary: Current research often equates a "source" with its generative architecture.<n>We propose redefining a source as a compositional of Architecture, Training Data, and other training factors affecting the generated speech.<n>This approach improves performance for partially seen sources and maintains robustness in fully open-set scenarios.
• Score: 7.66732820915854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of synthetic speech. However, current research often equates a "source" with its generative architecture. We propose redefining a source as a compositional tuple of Architecture, Training Data, and other training factors affecting the generated speech. We propose a framework using Structured Orthonormal Prototypes to minimize class overlap and intra-class variance. Our Subspace Partitioning strategy splits the embedding into architecture and data subspaces, while a residual subspace captures stochastic variability, enabling "compositional generalization" for novel factor combinations. This approach improves performance for partially seen sources and maintains robustness in fully open-set scenarios. MLAAD evaluations for Few-Shot open-set Identification show our approach significantly outperforms angular-margin baselines.

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