Tootfinder

Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
Oguzhan Kurnaz, Tomi Kinnunen, Cemal Hanilci
https://arxiv.org/abs/2509.12668 http…

Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
Despite improvements in automatic speaker verification (ASV), vulnerability against spoofing attacks remains a major concern. In this study, we investigate the integration of ASV and countermeasure (CM) subsystems into a modular spoof-aware speaker verification (SASV) framework. Unlike conventional single-stage score-level fusion methods, we explore the potential of a multi-stage approach that utilizes the ASV and CM systems in multiple stages. By leveraging ECAPA-TDNN (ASV) and AASIST (CM) sub…

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attri...
Firc, Chhibber, Mishra, Singh, Kinnunen, Malinka

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Xi Xuan, Zimo Zhu, Wenxin Zhang, Yi-Cheng Lin, Tomi Kinnunen
https://arxiv.org/abs/2508.09294

Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Advances in speech synthesis intensify security threats, motivating real-time deepfake detection research. We investigate whether bidirectional Mamba can serve as a competitive alternative to Self-Attention in detecting synthetic speech. Our solution, Fake-Mamba, integrates an XLSR front-end with bidirectional Mamba to capture both local and global artifacts. Our core innovation introduces three efficient encoders: TransBiMamba, ConBiMamba, and PN-BiMamba. Leveraging XLSR's rich linguistic repr…

WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection
Xi Xuan, Xuechen Liu, Wenxin Zhang, Yi-Cheng Lin, Xiaojian Lin, Tomi Kinnunen
https://arxiv.org/abs/2510.05305

WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection
Modern front-end design for speech deepfake detection relies on full fine-tuning of large pre-trained models like XLSR. However, this approach is not parameter-efficient and may lead to suboptimal generalization to realistic, in-the-wild data types. To address these limitations, we introduce a new family of parameter-efficient front-ends that fuse prompt-tuning with classical signal processing transforms. These include FourierPT-XLSR, which uses the Fourier Transform, and two variants based on …

Multilingual Source Tracing of Speech Deepfakes: A First Benchmark
Xi Xuan, Yang Xiao, Rohan Kumar Das, Tomi Kinnunen
https://arxiv.org/abs/2508.04143 https://

Multilingual Source Tracing of Speech Deepfakes: A First Benchmark
Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages. Current research has largely focused on detecting fake speech, but little attention has been given to tracing the source models used to generate it. This paper introduces the first benchmark for multili…

Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification
O\u{g}uzhan Kurnaz, Jagabandhu Mishra, Tomi H. Kinnunen, Cemal Hanil\c{c}i
https://arxiv.org/abs/2510.01818

Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification
Spoofing-robust speaker verification (SASV) combines the tasks of speaker and spoof detection to authenticate speakers under adversarial settings. Many SASV systems rely on fusion of speaker and spoof cues at embedding, score or decision levels, based on independently trained subsystems. In this study, we respect similar modularity of the two subsystems, by integrating their outputs using trainable back-end classifiers. In particular, we explore various approaches for directly optimizing the ba…

Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
Manasi Chhibber, Jagabandhu Mishra, Tomi H. Kinnunen
https://arxiv.org/abs/2509.24674 https://a…

Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
We propose a novel zero-shot source tracing framework inspired by advances in speaker verification. Specifically, we adapt the SSL-AASIST system for attack classification, ensuring that the attacks used for training are disjoint from those used to form fingerprint-trial pairs. For backend scoring in attack verification, we explore both zero-shot approaches (cosine similarity and Siamese) and few-shot approaches (MLP and Siamese). Experiments on our recently introduced STOPA dataset suggest that…