Dysarthric speech has several pathological characteristics, such as discontinuous
pronunciation, uncontrolled volume, slow speech, explosive pronunciation, improper …

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 …

Improving the accuracy of dysarthric speech recognition is a challenging research field due
to the high inter-and intra-speaker variability in disordered speech. In this work, we propose …

An assistive system for persons with vocal impairment due to dysarthria converts dysarthric
speech to normal speech or text. Because of the articulatory deficits, dysarthric speech …

We present an experimental comparison of seven state-of-the-art machine learning
algorithms for the task of semantic analysis of spoken input, with a special emphasis on …

Recognition of speech uttered by severe dysarthric speakers needs a robust learning
technique. One of the commonly used generative model-based classifiers for speech …

Besides the core learning algorithm itself, one major question in machine learning is how to
best encode given training data such that the learning technology can efficiently learn based …

With the tremendous improvements of automatic speech recognition systems worldwide,
efficient ways of recognizing dysarthric speech has emerged as a practical challenge …

This paper proposes a voice QR code for mobile devices. The QR code shows great
performance for error correction and recovers decoding errors caused by skewed image …

We present a semantic analysis technique for spoken input using Markov Logic Networks
(MLNs). MLNs combine graphical models with first-order logic. They are particularly suitable …