5 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…
Phonetically Explainable Speech Deepfake Detection
Manasi Chhibber, Jagabandhu Mishra, Tomi H. Kinnunen
Speech deepfake detection is predominantly treated as an opaque classification task where all temporal frames are aggregated equally. This ignores that different phonetic categorie…
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…
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…