Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

Modern speech enhancement algorithms achieve remarkable noise suppression by means
of large recurrent neural networks (RNNs). However, large RNNs limit practical deployment …

Numerous studies have investigated the effectiveness of audio-visual multimodal learning
for speech enhancement (AVSE) tasks, seeking a solution that uses visual data as auxiliary …

Many studies on deep learning-based speech enhancement (SE) utilizing the computational
auditory scene analysis method typically employs the ideal binary mask or the ideal ratio …

The most recent studies on deep learning based speech enhancement (SE) are focused on
improving denoising performance. However, successful SE applications require striking a …

Previous studies have confirmed the effectiveness of incorporating visual information into
speech enhancement (SE) systems. Despite improved denoising performance, two …

In digital health applications, speech offers advantages over other physiological signals, in
that it can be easily collected, transmitted, and stored using mobile and Internet of Things …

Numerous compression and acceleration techniques achieved state-of-the-art results for
classification tasks in speech processing. However, the same techniques produce …

Speech perception is the key to verbal communication. For people with hearing loss, the
capability to recognize speech is restricted, particularly in a noisy environment or the …

Owing to the recent advancements in deep learning technology, the performance of voice
conversion (VC) in terms of quality and similarity has significantly improved. However …