Dysarthria is a motor speech disability caused by weak muscles and organs involved in the
articulation process, thereby affecting the speech intelligibility of individuals. Because this …

Mainstream deep learning-based dysarthric speech detection approaches typically rely on
processing the magnitude spectrum of the short-time Fourier transform of input signals, while …

Although the UA-Speech and TORGO databases of control and dysarthric speech are
invaluable resources made available to the research community with the objective of …

Keyword spotting (KWS) is a task to recognize a keyword or a particular command in a
continuous audio stream, which can be effectively applied to a voice trigger system that …

Recently proposed automatic pathological speech classification techniques use
unsupervised auto-encoders to obtain a high-level abstract representation of speech. Since …

In recent years, deep learning methods in automatic pathological voice detection (APVD)
have gained satisfying results. However, most deep learning methods in APVD cannot …

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

Deep learning-based techniques for automatic dysarthric speech detection have recently
attracted interest in the research community. State-of-the-art techniques typically learn …

We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech
samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking …

Automatic classification of disordered speech can provide an objective tool for identifying the
presence and severity of speech impairment. Classification approaches can also help …