2 citations · 3 across the 9 of their papers we have counts for
14 papers
Neural Representations, Natural Connections: What Transfers From Human Speech Foundation Models to Animal Vocalizations?
Tomás Arias-Vergara, Christopher Hauer, Héloïse Brotier +3
Speech-pretrained models have shown promise in animal bioacoustics, but the factors governing their cross-species transfer remain poorly understood. We evaluate 15 frozen encoders…
Do Speech Representations Preserve Regional Accent Across Read and Spontaneous Speech?
Paula A. Perez-Toro, Tomas Arias-Vergara, Annette Schwarz +4
Regional accent cues can be captured under matched conditions, but it remains unclear whether they persist between read and spontaneous speech. We study RVG1, with 500 German speak…
Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow
Changhun Kim, Timon Conrad, Redwanul Karim +7
Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imply physically consistent sol…
A Systematic Evaluation of Machine Learning Methods for Fault Detection and Line Identification in Electrical Power Grids
Julian Oelhaf, Georg Kordowich, Paula Andrea Pérez-Toro +4
The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional rela…
Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis
Souranil Kahali, Rituparna Bose, Abner Hernandez +4
Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilingual use cases. Although large-scale pret…
LoGSAM: Parameter-Efficient Cross-Modal Grounding for MRI Segmentation
Mohammad Robaitul Islam Bhuiyan, Sheethal Bhat, Melika Qahqaie +4
Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are essential for planning therapy and guiding surgical decisions. To address this, we p…
Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Prediction
Redwanul Karim, Changhun Kim, Timon Conrad +7
Accurate AC power flow (AC-PF) prediction under domain shift is critical when models trained on medium-voltage (MV) grids are deployed on high-voltage (HV) networks. Existing physi…
Confidence-Guided Error Correction for Disordered Speech Recognition
Abner Hernandez, Tomás Arias Vergara, Andreas Maier +1
We investigate the use of large language models (LLMs) as post-processing modules for automatic speech recognition (ASR), focusing on their ability to perform error correction for…
SpeechCT-CLIP: Distilling Text-Image Knowledge to Speech for Voice-Native Multimodal CT Analysis
Lukas Buess, Jan Geier, David Bani-Harouni +6
Spoken communication plays a central role in clinical workflows. In radiology, for example, most reports are created through dictation. Yet, nearly all medical AI systems rely excl…
Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Tomás Arias-Vergara +2
Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate c…
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim, Timon Conrad, Redwanul Karim +6
Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of sce…
VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI
Daiqi Liu, Johannes Enk, Maureen Stone +7
Accurate segmentation of articulatory structures in real-time MRI (rtMRI) remains challenging, as existing methods rely primarily on visual cues and overlook complementary informat…
Perceptual implications of automatic anonymization in pathological speech
Soroosh Tayebi Arasteh, Saba Afza, Tri-Thien Nguyen +11
Automatic anonymization is increasingly used to enable ethical sharing of clinical speech, yet its perceptual and clinical consequences remain undercharacterized. We present a huma…
A Speech-to-Video Synthesis Approach Using Spatio-Temporal Diffusion for Vocal Tract MRI
Paula Andrea Pérez-Toro, Tomás Arias-Vergara, Fangxu Xing +9
Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treat…