Automatic recognition of elderly and disordered speech remains a highly challenging task to
date. Such data is not only difficult to collect in large quantities, but also exhibits a significant …

Discriminative training techniques define state-of-the-art performance for automatic speech
recognition systems. However, they are inherently prone to overfitting, leading to poor …

The recent proposed time-delay deep neural network (TDNN) acoustic models trained with
lattice-free maximum mutual information (LF-MMI) criterion have been shown to give …

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 …

Early diagnosis of Neurocognitive Disorder (NCD) is crucial in facilitating preventive care
and timely treatment to delay further progression. This paper presents the development of a …

Automatic recognition of disordered and elderly speech remains a highly challenging task to
date due to the difficulty in collecting such data in large quantities. This paper explores a …

State-of-the-art automatic speech recognition (ASR) system development is data and
computation intensive. The optimal design of deep neural networks (DNNs) for these …

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

State-of-the-art neural language models (LMs) represented by Transformers are highly
complex. Their use of fixed, deterministic parameter estimates fail to account for model …

Speaker adaptation techniques play a key role in reducing the mismatch between automatic
speech recognition (ASR) systems and target users. Deep neural network (DNN) acoustic …