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) …

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) …

Dysarthria is a motor speech disorder resulting from impairment in muscles responsible for
speech production, often characterized by slurred or slow speech resulting in low …

Dysarthric speech recognition requires a learning technique that is able to capture dysarthric
speech specific features. Dysarthric speech is considered as speech with source distortion …

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 …

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 …

Acoustic modelling for automatic dysarthric speech recognition (ADSR) is a challenging
task. Data deficiency is a major problem and substantial differences between the typical and …

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 …

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

Improving the accuracy of personalised speech recognition for speakers with dysarthria is a
challenging research field. In this paper, we explore an approach that non-linearly modifies …