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arXiv · 2607.16369

Component-Level Ensemble Fusion for Speech and Environmental Sound Deepfake Detection

André Runewicz·Karla Schäfer·Martin Steinebach
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Abstract

This paper describes our submission to the ICME 2026 ESDD2 challenge on environment-aware speech and sound deepfake detection. The task requires five-class classification of audio clips in which speech, environmental sound, both components, or neither component may be spoofed. We propose a component-level ensemble system based on four publicly available pre-trained anti-spoofing models: XLSR-Mamba, DF-Arena, SLS, and TCM-ADD. Each model is fine-tuned on the official CompSpoofV2 development data using three binary heads for original, speech, and environmental sound detection. We further train RawBoost-augmented variants and combine selected checkpoints using margin-space score fusion. A component-wise fusion strategy with lightweight head- and class-bias calibration yields our best configuration, reaching 0.7715 macro-F1 on the evaluation set and 0.7828 macro-F1 on the test set, ranking 5th out of 31 teams in the final ranking phase and substantially outperforming the official baseline.

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André Runewicz, Karla Schäfer, Martin Steinebach. 2026-07-17. Component-Level Ensemble Fusion for Speech and Environmental Sound Deepfake Detection. https://arxiv.org/abs/2607.16369

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