Identifying the severity of dysarthria is considered a diagnostic step in monitoring the
patient's progress and a beneficial step in the transcription of dysarthric speech. In this …

Automated dysarthria severity identification can aid clinicians in monitoring the patient's
progress, and can improve the performance of dysarthric speech recognition systems. In this …

Assessing the severity level of dysarthria can provide an insight into the patient's
improvement, assist pathologists to plan therapy, and aid automatic dysarthric speech …

Automatic detection and severity level classification of dysarthria from speech enables non-
invasive and effective diagnosis that helps clinical decisions about medication and therapy …

Dysarthria is a neuro-motor speech disorder that renders speech unintelligible, in
proportional to its severity. Assessing the severity level of dysarthria, apart from being a …

Automatic detection and severity level classification of dysarthria directly from acoustic
speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained …

Dysarthria is a speech disorder that can significantly impact a person's daily life, and yet may
be amenable to therapy. To automatically detect and classify dysarthria, researchers have …

Automatic assessment of dysarthric speech is essential for sustained treatments and
rehabilitation. However, obtaining atypical speech is challenging, often leading to data …

Data scarcity hinders research on dysarthria severity classification due to the limited size of
datasets. While the crosslingual approach has been applied to alleviate the problem, the …

This paper presents a novel approach for automated dysarthria detection and severity
assessment using a variable short-time Fourier transform layered convolutional neural …