3 citations · 3 across the 9 of their papers we have counts for
10 papers
Explainability by Design: Structured Kolmogorov-Arnold Networks over Probabilistic Attributes for Speech Deepfake Source Tracing
Hoang H. Pham, Manasi Chhibber, Tomi H. Kinnunen
Modern speech synthesizers can produce highly realistic speech, making source tracing (i.e. identifying the generator behind a spoofed utterance) increasingly important for forensi…
Kinship Verification Using Voice
Jagabandhu Mishra, Tomi H. Kinnunen
Kinship verification (KV) from voice, the task of determining whether two speakers are biologically related, has received only little attention. Our work establishes a foundational…
Joint Optimization of ASV and CM tasks: BTUEF Team's Submission for WildSpoof Challenge
Oguzhan Kurnaz, Jagabandhu Mishra, Tomi Kinnunen +1
Spoofing-aware speaker verification (SASV) jointly addresses automatic speaker verification and spoofing countermeasures to improve robustness against adversarial attacks. In this…
Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics
Md Sahidullah, Hye-jin Shim, Rosa Gonzalez Hautamäki +1
The widespread adoption of deep-learning models in data-driven applications has drawn attention to the potential risks associated with biased datasets and models. Neglected or hidd…
Joint Optimization of Speaker and Spoof Detectors for Spoofing-Robust Automatic Speaker Verification
Oğuzhan Kurnaz, Jagabandhu Mishra, Tomi H. Kinnunen +1
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…
Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
Oguzhan Kurnaz, Tomi Kinnunen, Cemal Hanilci
Despite improvements in automatic speaker verification (ASV), vulnerability against spoofing attacks remains a major concern. In this study, we investigate the integration of ASV a…
Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
Manasi Chhibber, Jagabandhu Mishra, Tomi H. Kinnunen
We propose a novel zero-shot source tracing framework inspired by speaker verification. We adapt SSL-AASIST for attack classification, enhancing embeddings with AAM loss and RegMix…
Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components
Jagabandhu Mishra, Manasi Chhibber, Hye-jin Shim +1
We propose an explainable probabilistic framework for characterizing spoofed speech by decomposing it into probabilistic attribute embeddings. Unlike raw high-dimensional counterme…
Explaining Speaker and Spoof Embeddings via Probing
Xuechen Liu, Junichi Yamagishi, Md Sahidullah +1
This study investigates the explainability of embedding representations, specifically those used in modern audio spoofing detection systems based on deep neural networks, known as…
An Explainable Probabilistic Attribute Embedding Approach for Spoofed Speech Characterization
Manasi Chhibber, Jagabandhu Mishra, Hyejin Shim +1
We propose a novel approach for spoofed speech characterization through explainable probabilistic attribute embeddings. In contrast to high-dimensional raw embeddings extracted fro…