Many speech features and models, including Deep Neural Networks (DNN), are used for
classification tasks between healthy and pathological speech with the Saarbruecken Voice …

Deep learning techniques such as convolutional neural networks (CNN) have been
successfully applied to identify pathological voices. However, the major disadvantage of …

Language is one the earliest capacities affected by cognitive change. To monitor that
change longitudinally, we have developed a web portal for remote linguistic data …

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

Goal: Numerous studies had successfully differentiated normal and abnormal voice
samples. Nevertheless, further classification had rarely been attempted. This study proposes …

There has been a surge of interest in leveraging speech as a marker of health for a wide
spectrum of conditions. The underlying premise is that any neurological, mental, or physical …

This study proposes two multimodal frameworks to classify pathological voice samples by
combining acoustic signals and medical records. In the first framework, acoustic signals are …

Voice disorders, such as dysphonia, are common among the general population. These
pathologies often remain untreated until they reach a high level of severity. Assisting the …

While pre-trained automatic speech recognition (ASR) systems demonstrate impressive
performance on matched domains, their performance often degrades when confronted with …

Objective: Voice disorders significantly compromise individuals' ability to speak in their daily
lives. Without early diagnosis and treatment, these disorders may deteriorate drastically …