Speech-based approaches for assessing Parkinson's Disease (PD) often rely on feature
extraction for automatic classification or detection. While many studies prioritize accuracy by …

Numerous studies proposed methods to detect Parkinson's disease (PD) via speech
analysis. However, existing corpora often lack prodromal recordings, have small sample …

Deep learning-based pathological speech detection approaches are gaining popularity as a
diagnostic tool to support time-consuming and subjective clinical assessments. While these …

Recent works in pathological speech analysis have increasingly relied on powerful self-
supervised speech representations, leading to promising results. However, the complex …

Automatic pathological speech detection relies on deep learning (DL), showing promising
performance for various pathologies. Despite the critical importance of robustness in …

Accurately extracting clinical information from speech is critical to the diagnosis and
treatment of many neurological conditions. As such, there is interest in leveraging AI for …

Although automatic pathological speech detection approaches show promising results when
clean recordings are available, they are vulnerable to additive noise. Recently it has been …

Recent findings show that pre-trained wav2vec 2.0 models are reliable feature extractors for
various speaker characteristics classification tasks. We show that latent representations …

In this work, we investigate Speech Foundation Models (SFMs) for Parkinson's Disease (PD)
detection. We explore two main approaches:(1) using SFMs as frozen feature extractors …

Automated depression detection (ADD) from speech signals allows early identification and
intervention, reducing costs to medical healthcare. However, most of the existing ADD …