This paper investigates the ability of deep neural networks (DNNs) to improve the automatic
recognition of dysarthric speech through the use of convolutional neural networks (CNNs) …

Transcribing dysarthric speech into text is still a challenging problem for the state-of-the-art
techniques or commercially available speech recognition systems. Improving the accuracy …

Dysarthria is a motor speech disorder that impedes the physical production of speech.
Speech in patients with dysarthria is generally characterized by poor articulation, breathy …

Automatic speech recognition (ASR) transcribes the human voice into a text automatically.
Recently, ASR systems has reached, almost, the human performance in specific scenarios …

Dysarthria is a motor speech disorder, resulting in mumbled, slurred or slow speech that is
generally difficult to understand by both humans and machines. Traditional Automatic …

Dysarthria is a motor speech disorder and the most common neurodegenerative disease
characterized by low volume in precise articulation, poor coordination of respiratory and …

In this paper, we use empirical mode decomposition and Hurst-based mode selection
(EMDH) along with deep learning architecture using a convolutional neural network (CNN) …

Dysarthria is a neurological impairment of controlling the motor speech articulators that
compromises the speech signal. Automatic Speech Recognition (ASR) can be very helpful …

Automatic speech recognition (ASR) can be very helpful for speakers who suffer from
dysarthria, a neurological disability that damages the control of motor speech articulators …

Assistive speech-based technologies can improve the quality of life for people affected with
dysarthria, a motor speech disorder. In this paper, we explore multiple ways to improve …