Similar to humans' cognitive ability to generalize knowledge and skills, self-supervised
learning (SSL) targets discovering general representations from large-scale data. This …

Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

Many self-supervised speech models, varying in their pre-training objective, input modality,
and pre-training data, have been proposed in the last few years. Despite impressive …

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

Speech representations learned from Self-supervised learning (SSL) models can benefit
various speech processing tasks. However, utilizing SSL representations usually requires …

We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised
speech representation for better performance, generalization, and efficiency. The challenge …

Self-Supervised Learning (SSL) from speech data has produced models that have achieved
remarkable performance in many tasks, and that are known to implicitly represent many …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
improving performance and data efficiency on various speech tasks. However, these …

Data-driven speech processing models usually perform well with a large amount of text
supervision, but collecting transcribed speech data is costly. Therefore, we propose Speech …

Self-supervised learning (SSL) representation for speech has achieved state-of-the-art
(SOTA) performance on several downstream tasks. However, there remains room for …