P. A. Pérez-Toro

Acoustic noise, even when not diagnostically informative, can systematically manipulate Alzheimer's predictions in SSL models, revealing a critical vulnerability in current speech-based assessments.

PANDA achieves a remarkable AUC of 0.893 in Alzheimer's classification, outperforming traditional methods even with zero auxiliary data at inference.

A standardized evaluation framework reveals that even near-perfect machine learning scores in power system protection can be misleading without consistent assessment criteria.

Fine-tuning Whisper models for multilingual medical ASR reveals that the best performance hinges on the adaptation strategy, with surprising shifts in internal representations based on language context.

Layer selection for speech-based PD detection is more about the dataset than the model architecture, revealing a critical flaw in current approaches.

PhonoQ-derived features boost phonological classification accuracy, revealing intricate speech patterns that traditional models miss.

SSL embeddings, typically superior for speech tasks, surprisingly lose their edge to hand-crafted acoustic features when classifying mild cognitive impairment from speech, challenging assumptions about representation learning in clinical applications.