Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Despite the rapid progress of automatic speech recognition (ASR) technologies in the past
few decades, recognition of disordered speech remains a highly challenging task to date …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Dysarthria is a disorder that affects an individual's speech intelligibility due to the paralysis of
muscles and organs involved in the articulation process. As the condition is often associated …

Recently, we have witnessed Deep Learning methodologies gaining significant attention for
severity-based classification of dysarthric speech. Detecting dysarthria, quantifying its …

Disordered speech recognition is a highly challenging task. The underlying neuro-motor
conditions of people with speech disorders, often compounded with co-occurring physical …

This paper presents an improved transfer learning framework applied to robust personalised
speech recognition models for speakers with dysarthria. As the baseline of transfer learning …

Automatic methods for the detection of pathological voice from healthy speech can be
considered as potential clinical tools for medical treatment. This study investigates the …

Automatic recognition of disordered speech remains a highly challenging task to date. The
underlying neuro-motor conditions, often compounded with co-occurring physical …

Dysarthric speech recognition has posed major challenges due to lack of training data and
heavy mismatch in speaker characteristics. Recent ASR systems have benefited from readily …