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

Speech signals, typically sampled at rates in the tens of thousands per second, contain
redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech …

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 …

Speech and text are two major forms of human language. The research community has
been focusing on mapping speech to text or vice versa for many years. However, in the field …

Speaker individuality information is among the most critical elements within speech signals.
By thoroughly and accurately modeling this information, it can be utilized in various …

Many self-supervised speech models (S3Ms) have been introduced over the last few years,
producing performance and data efficiency improvements for a variety of speech tasks …

Visual Speech Recognition (VSR) is the task of predicting spoken words from silent lip
movements. VSR is regarded as a challenging task because of the insufficient information …

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) is at the origin of unprecedented improvements in many
different domains including computer vision and natural language processing. Speech …

Self-supervised models have had great success in learning speech representations that can
generalize to various downstream tasks. However, most self-supervised models require a …