Automatic recognition of dysarthric speech remains a highly challenging task to date. Neuro-
motor conditions and co-occurring physical disabilities create difficulty in large-scale data …

Dysarthria, a speech disorder often caused by neurological damage, compromises the
control of vocal muscles in patients, making their speech unclear and communication …

A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to
both speaker-identity associated factors such as gender, and speech impairment severity …

Post-stroke speech and language deficits (aphasia) significantly impact patients' quality of
life. Many with mild symptoms remain undiagnosed, and the majority do not receive the …

Self-supervised learning (SSL) based speech foundation models have been applied to a
wide range of ASR tasks. However, their application to dysarthric and elderly speech via …

In this paper, we propose a novel method for speaker adaptation in lip reading, motivated by
two observations. Firstly, a speaker's own characteristics can always be portrayed well by …

Automatic recognition of disordered speech remains a highly challenging task to date due to
data scarcity. This paper presents a reinforcement learning (RL) based on-the-fly data …

The application of data-intensive automatic speech recognition (ASR) technologies to
dysarthric and elderly adult speech is confronted by their mismatch against healthy and …

Deep learning-based medical diagnosis models heavily rely on large-scale datasets
encompassing diverse aspects of a patient's condition. However, the scarcity of such …

Automatic speech recognition systems based on deep learning are mainly trained under
empirical risk minimization (ERM). Since ERM utilizes the averaged performance on the …