The recently proposed Conformer model has become the de facto backbone model for
various downstream speech tasks based on its hybrid attention-convolution architecture that …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Conformer-based models have become the dominant end-to-end architecture for speech
processing tasks. With the objective of enhancing the conformer architecture for efficient …

This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment
(IQA), held in conjunction with the New Trends in Image Restoration and Enhancement …

Abstract End-to-end Automatic Speech Recognition (ASR) systems based on neural
networks have seen large improvements in recent years. The availability of large scale hand …

Significant research has been conducted during the last decade on the application of
machine learning for speech processing, particularly speech recognition. However, in recent …

Transformers have dominated empirical machine learning models of natural language
processing. In this paper, we introduce basic concepts of Transformers and present key …

We improve on the popular conformer architecture by replacing the depthwise temporal
convolutions with diagonal state space (DSS) models. DSS is a recently introduced variant …

The study of the attention mechanism has sparked interest in many fields, such as language
modeling and machine translation. Although its patterns have been exploited to perform …

Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA)
methods due to the expenses and complexity required to handle many languages. This …