2 citations · 2 across the 10 of their papers we have counts for
11 papers
ASR ensembling for phoneme intelligibility evaluation of speech anonymizers
Victor Ménestrel, Sebastian Möller, Slim Ouni +1
We present the first phoneme-level intelligibility evaluation of speech anonymizers, assessing the performance of ASR-ensemble-based metrics against measured intelligibility from a…
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…
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…
Generalizable Audio Spoofing Detection using Non-Semantic Representations
Arnab Das, Yassine El Kheir, Carlos Franzreb +3
Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need…
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…
Two Views, One Truth: Spectral and Self-Supervised Features Fusion for Robust Speech Deepfake Detection
Yassine El Kheir, Arnab Das, Enes Erdem Erdogan +3
Recent advances in synthetic speech have made audio deepfakes increasingly realistic, posing significant security risks. Existing detection methods that rely on a single modality,…
Private kNN-VC: Interpretable Anonymization of Converted Speech
Carlos Franzreb, Arnab Das, Tim Polzehl +1
Speaker anonymization seeks to conceal a speaker's identity while preserving the utility of their speech. The achieved privacy is commonly evaluated with a speaker recognition mode…
BiCrossMamba-ST: Speech Deepfake Detection with Bidirectional Mamba Spectro-Temporal Cross-Attention
Yassine El Kheir, Tim Polzehl, Sebastian Möller
We propose BiCrossMamba-ST, a robust framework for speech deepfake detection that leverages a dual-branch spectro-temporal architecture powered by bidirectional Mamba blocks and mu…
Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection
Yassine El Kheir, Youness Samih, Suraj Maharjan +2
This paper conducts a comprehensive layer-wise analysis of self-supervised learning (SSL) models for audio deepfake detection across diverse contexts, including multilingual datase…