Many acoustic features and machine learning models have been studied to build automatic
detection systems to distinguish dysarthric speech from healthy speech. These systems can …

Advancements in deep learning speech representations have facilitated the effective use of
extensive unlabeled speech datasets for Parkinson's disease (PD) modeling with minimal …

Speech is known to carry health-related attributes, which has emerged as a novel venue for
remote and long-term health monitoring. However, existing models are usually tailored for a …

Despite the promising performance of state of the art approaches for Parkinsons Disease
(PD) detection, these approaches often analyze individual speech segments in isolation …

Over the past decade, the prevalence of neurological diseases has significantly risen due to
population growth and aging. Individuals suffering from spastic paralysis, brain attack, and …

Automatic pathological speech detection approaches yield promising results in identifying
various pathologies. These approaches are typically designed and evaluated for …

Head and Neck Cancers (HNC) significantly impact patients' ability to speak, affecting their
quality of life. Commonly used metrics for assessing pathological speech are subjective …

Les cancers des voies aérodigestives supérieures (VADS) ont un impact significatif sur la
capacité des patients à s' exprimer, ce qui affecte leur qualité de vie. Les évaluations …