Deep learning has emerged as a powerful tool in various domains, revolutionising machine
learning research. However, one persistent challenge is the scarcity of labelled training …

The speech representations learned from large-scale unlabeled data have shown better
generalizability than those from supervised learning and thus attract a lot of interest to be …

In recent years, speaker recognition systems based on raw waveform inputs have received
increasing attention. However, the performance of such systems are typically inferior to the …

In self-supervised learning for speaker recognition, pseudo labels are useful as the
supervision signals. It is a known fact that a speaker recognition model doesn't always …

The automatic speaker verification task has achieved great success using deep learning
approaches with a large-scale, manually annotated dataset. However, collecting a …

Speech quality assessment has been a critical issue in speech processing for decades.
Existing automatic evaluations usually require clean references or parallel ground truth data …

For self-supervised speaker verification, the quality of pseudo labels decides the upper
bound of the system due to the massive unreliable labels. In this work, we propose dynamic …

Training speaker-discriminative and robust speaker verification systems without speaker
labels is still challenging and worthwhile to explore. In this study, we propose an effective …

Self-supervised pretraining for Automated Speech Recognition (ASR) has shown varied
degrees of success. In this paper, we propose to jointly learn representations during …

Self-supervised learning (SSL) has drawn an increased attention in the field of speech
processing. Recent studies have demonstrated that contrastive learning is able to learn …