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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.SD2026

Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models

Yixuan Xiao, Cheng-Wei Lin, Xin Wang +5

The paper introduces Evidence Subspace Projection, a technique that quantifies how different evidence factors (like attack type, codec, gender, transmission) explain the decisions…

cs.SD2026

DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection

Yassine El Kheir, Arnab Das, Yixuan Xiao +6

Speech deepfake detection is a well-established research field with different models, datasets, and training strategies. However, the lack of standardized implementations and evalu…

cs.SD2026

DFKI-Speech System for WildSpoof Challenge: A robust framework for SASV In-the-Wild

Arnab Das, Yassine El Kheir, Enes Erdem Erdogan +3

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 fram…

eess.AS2026

Content Leakage in LibriSpeech and Its Impact on the Privacy Evaluation of Speaker Anonymization

Carlos Franzreb, Arnab Das, Tim Polzehl +1

Speaker anonymization aims to conceal a speaker's identity, without considering the linguistic content. In this study, we reveal a weakness of Librispeech, the dataset that is comm…

eess.AS2026

Improving the Speaker Anonymization Evaluation's Robustness to Target Speakers with Adversarial Learning

Carlos Franzreb, Arnab Das, Tim Polzehl +1

The current privacy evaluation for speaker anonymization often overestimates privacy when a same-gender target selection algorithm (TSA) is used, although this TSA leaks the speake…

cs.SD2025

A Parameter-Efficient Multi-Scale Convolutional Adapter for Synthetic Speech Detection

Yassine El Kheir, Fabian Ritter-Guttierez, Arnab Das +2

Recent synthetic speech detection models typically adapt a pre-trained SSL model via finetuning, which is computationally demanding. Parameter-Efficient Fine-Tuning (PEFT) offers a…