8 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…
Gendered Prompting and LLM Code Review: How Gender Cues in the Prompt Shape Code Quality and Evaluation
Lynn Janzen, Üveys Eroglu, Dorothea Kolossa +4
LLMs are increasingly embedded in programming workflows, from code generation to automated code review. Yet, how gendered communication styles interact with LLM-assisted programmin…
Optimal Calibration of the Endpoint-corrected Hilbert Transform
Eike Osmers, Dorothea Kolossa
Accurate, low-latency estimates of the instantaneous phase of oscillations are essential for closed-loop sensing and actuation, including (but not limited to) phase-locked neurosti…
Audio-Visual Speech Enhancement for Spatial Audio - Spatial-VisualVoice and the MAVE Database
Danielle Yaffe, Ferdinand Campe, Prachi Sharma +2
Audio-visual speech enhancement (AVSE) has been found to be particularly useful at low signal-to-noise (SNR) ratios due to the immunity of the visual features to acoustic noise. Ho…
MisinfoTeleGraph: Network-driven Misinformation Detection for German Telegram Messages
Lu Kalkbrenner, Veronika Solopova, Steffen Zeiler +2
Connectivity and message propagation are central, yet often underutilized, sources of information in misinformation detection -- especially on poorly moderated platforms such as Te…
A database to support the evaluation of gender biases in GPT-4o output
Luise Mehner, Lena Alicija Philine Fiedler, Sabine Ammon +1
The widespread application of Large Language Models (LLMs) involves ethical risks for users and societies. A prominent ethical risk of LLMs is the generation of unfair language out…
How desirable is alignment between LLMs and linguistically diverse human users?
Pia Knoeferle, Sebastian Möller, Dorothea Kolossa +2
We discuss how desirable it is that Large Language Models (LLMs) be able to adapt or align their language behavior with users who may be diverse in their language use. User diversi…
Extending Information Bottleneck Attribution to Video Sequences
Veronika Solopova, Lucas Schmidt, Dorothea Kolossa
We introduce VIBA, a novel approach for explainable video classification by adapting Information Bottlenecks for Attribution (IBA) to video sequences. While most traditional explai…