DFKI-Speech System for WildSpoof Challenge: A robust framework for SASV In-the-Wild
On the map: Mixture-of-experts → Multimodal MoE models
Abstract (arXiv)
This paper presents the DFKI-Speech system developed for the WildSpoof Challenge under the Spoofing aware Automatic Speaker Verification (SASV) track. We propose a robust SASV framework in which a spoofing detector and a speaker verification (SV) network operate in tandem. The spoofing detector employs a self-supervised speech embedding extractor as the frontend, combined with a state-of-the-art graph neural network backend. In addition, a top-3 layer based mixture-of-experts (MoE) is used to fuse high-level and low-level features for effective spoofed utterance detection. For speaker verification, we adapt a low-complexity convolutional neural network that fuses 2D and 1D features at multiple scales, trained with the SphereFace loss. Additionally, contrastive circle loss is applied to adaptively weight positive and negative pairs within each training batch, enabling the network to better distinguish between hard and easy sample pairs. Finally, fixed imposter cohort based AS Norm score normalization and model ensembling are used to further enhance the discriminative capability of the speaker verification system.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal MoE models, one of 12 directions in Mixture-of-experts. Builds on 8 of its 9 references that are in the map. First posted on arXiv 2 Feb 2026.
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- 2017In Defense of the Triplet Loss for Person Re-Identificationcited by 784
- 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589
- 2020Circle Loss: A Unified Perspective of Pair Similarity Optimizationcited by 402
- 2021SphereFace2: Binary Classification is All You Need for Deep Face Recognitioncited by 42
- 2024Reshape Dimensions Network for Speaker Recognitioncited by 25
- 2024SpoofCeleb: Speech Deepfake Detection and SASV In The Wildcited by 22
- 2022Frequency and Multi-Scale Selective Kernel Attention for Speaker Verificationcited by 10
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- 2026From Self-Supervised Speech Models to Mixture-of-Experts for Robust Anti-Spoofing
- 2025Noise-Conditioned Mixture-of-Experts Framework for Robust Speaker Verification
- 2026Speaker Identity in Non-Verbal Vocalizations: Conditional Distillation and Mixture of Experts Approach
- 2025Robust TTS Training via Self-Purifying Flow Matching for the WildSpoof 2026 TTS Track
- 2024MASV: Speaker Verification with Global and Local Context Mamba
- 2025MoLEx: Mixture of LoRA Experts in Speech Self-Supervised Models for Audio Deepfake Detection
- 2025GLAD: Global-Local Aware Dynamic Mixture-of-Experts for Multi-Talker ASR
- 2025Benchmarking Fake Voice Detection in the Fake Voice Generation Arms Race ↗