Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Self-supervised learning (SSL) has recently allowed leveraging large datasets of unlabeled
speech signals to reach impressive performance on speech tasks using only small amounts …

To leverage the copious amount of data from source tasks and overcome the scarcity of the
target task samples, representation learning based on multi-task pretraining has become a …

Self-supervised learning (SSL) leverages large datasets of unlabeled speech to reach
impressive performance with reduced amounts of annotated data. The high number of …

Self-supervised learning (SSL) for rich speech representations has achieved empirical
success in low-resource Automatic Speech Recognition (ASR) and other speech processing …

Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech
Recognition (ASR) performance in low-resource settings. In this context, it has been …

Computational machine intelligence approaches have enabled a variety of music-centric
technologies in support of creating, sharing and interacting with music content. A strong …

Limited diversity in standardized benchmarks for evaluating audio representation learning
(ARL) methods may hinder systematic comparison of current methods' capabilities. We …

The design of the feature extraction process and training strategy are crucial aspects of
achieving high-performance facial expression recognition (FER). Although the introduction …

Different self-supervised tasks (SSL) reveal different features from the data. The learned
feature representations can exhibit different performance for each downstream task. In this …