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 (SE) networks typically implement noise suppression through
time-frequency masking, latent representation masking, or discriminative signal prediction …

Meetings are a pervasive method of communication within all types of companies and
organizations, and using remote collaboration systems to conduct meetings has increased …

Speech Emotion Recognition (SER) typically relies on utterance-level solutions. However,
emotions conveyed through speech should be considered as discrete speech events with …

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 …

The zero-shot text-to-speech (TTS) method, based on speaker embeddings extracted from
reference speech using self-supervised learning (SSL) speech representations, can …

Pre-trained self-supervised learning (SSL) models have achieved remarkable success in
various speech tasks. However, their potential in target speech extraction (TSE) has not …

AV-HuBERT, a multi-modal self-supervised learning model, has been shown to be effective
for categorical problems such as automatic speech recognition and lip-reading. This …

Audio classification and restoration are among major downstream tasks in audio signal
processing. However, restoration derives less of a benefit from pretrained models compared …

In recent years, self-supervised learning (SSL) has achieved tremendous success in various
speech tasks due to its power to extract representations from massive unlabeled data …