30 citations · 41 across the 19 of their papers we have counts for
19 papers
Textual Acoustic Grounding for Generalizable LLM-Based Deepfake Voice Detection
Yassine El Kheir, Xin Wang, Wanqing Ge +3
Deepfake voice detection suffers from poor generalization across unseen domains. While Audio Large Language Models (ALLMs) show promise, the modality gap between continuous audio e…
Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models
Yixuan Xiao, Cheng-Wei Lin, Xin Wang +5
Self-supervised learning (SSL) models are widely used as feature extractors for state-of-the-art audio deepfake detection, but it remains unclear how to directly and quantitatively…
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…
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…
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…
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…