Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to
benchmark the performance of Self-Supervised Learning (SSL) models on various speech …

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 …

End-to-end speech translation (ST) is the task of translating speech signals in the source
language into text in the target language. As a cross-modal task, end-to-end ST is difficult to …

Large language models (LLMs) have gained considerable attention for Artificial Intelligence
Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct …

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

The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge
expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in …

Distilled self-supervised models have shown competitive performance and efficiency in
recent years. However, there is a lack of experience in jointly distilling multiple self …

Audio-visual representation learning aims to develop systems with human-like perception by
utilizing correlation between auditory and visual information. However, current models often …

Hidden-unit BERT (HuBERT) is a widely-used self-supervised learning (SSL) model in
speech processing. However, we argue that its fixed 20ms resolution for hidden …

Speech emotion recognition (SER) is a pivotal technology for human-computer interaction
systems. However, 80.77% of SER papers yield results that cannot be reproduced. We …