Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Automatic Speech Recognition (ASR) systems often struggle with transcribing child speech
due to the lack of large child speech datasets required to accurately train child-friendly ASR …

In this study, we aim to explore efficient tuning methods for speech self-supervised learning.
Recent studies show that self-supervised learning (SSL) can learn powerful representations …

Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased
attention in the automatic speech recognition (ASR) community. Typical SSL methods …

Despite recent advancements in deep learning technologies, Child Speech Recognition
remains a challenging task. Current Automatic Speech Recognition (ASR) models require …

Recently, end-to-end models have been widely used in automatic speech recognition (ASR)
systems. Two of the most representative approaches are connectionist temporal …

Data scarcity makes low-resource speech recognition systems suffer from severe overfitting.
Although fine-tuning addresses this issue to some extent, it leads to parameter-inefficient …

Automatic Speech Recognition (ASR) systems have progressed significantly in their
performance on adult speech data; however, transcribing child speech remains challenging …

Modern end-to-end Automatic Speech Recognition (ASR) systems struggle to recognise
children's speech. This challenge is due to the high acoustic variability in children's voices …

Developing a practically-robust automatic speech recognition (ASR) is challenging since the
model should not only maintain the original performance on clean samples, but also achieve …