The discrepancy between the cost function used for training a speech enhancement model
and human auditory perception usually makes the quality of enhanced 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 …

Speech enhancement (SE) performance has improved considerably owing to the use of
deep learning models as a base function. Herein, we propose a perceptual contrast …

Individuals with hearing impairments face challenges in their ability to comprehend speech,
particularly in noisy environments. The aim of this study is to explore the effectiveness of …

Transformer architecture has enabled recent progress in speech enhancement. Since
Transformers are position-agostic, positional encoding is the de facto standard component …

An important development in deep learning from the earliest MLPs has been a move
towards architectures with structural inductive biases which enable the model to keep …

This work aims to study a scalable state-space model (SSM), Mamba, for the speech
enhancement (SE) task. We exploit a Mamba-based regression model to characterize …

The transformer architecture has shown great capability in learning long-term dependency
and works well in multiple domains. However, transformer has been less considered in …

A deep neural network (DNN)-based speech enhancement (SE) aiming to maximize the
performance of an automatic speech recognition (ASR) system is proposed in this paper. In …

Although deep learning (DL) has achieved notable progress in speech enhancement (SE),
further research is still required for a DL-based SE system to adapt effectively and efficiently …