In recent years, rapid advancements have taken place for automatic speech recognition
(ASR) systems and devices. Though ASR technologies have increased, the accessibility of …

Abstract Automatic Speech Recognition technology (ASR) has recently witnessed a
paradigm shift with respect to performance accuracy. Nevertheless, impaired speech …

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 …

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting
normal speech in recent decades, accurate recognition of dysarthric and elderly speech …

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

Previous studies on the automatic classification of voice disorders have mostly investigated
the binary classification task, which aims to distinguish pathological voice from healthy …

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

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 …

We present a novel typical-to-atypical voice conversion approach (DuTa-VC), which (i) can
be trained with nonparallel data (ii) first introduces diffusion probabilistic model (iii) …

We present a voice conversion framework that converts normal speech into dysarthric
speech while preserving the speaker identity. Such a framework is essential for (1) clinical …