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

The use of deep neural networks (DNNs) has dramatically elevated the performance of
speech enhancement over the last decade. However, to achieve strong enhancement …

This paper presents recent advances in low-latency, single-channel, deep neural network-
based speech enhancement systems. The sources of latency and their acceptable values in …

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

The use of deep neural networks (DNNs) has dramatically improved the performance of
speech enhancement in the past decade. However, a large DNN is typically required to …

The field of computational paralinguistics emerged from automatic speech processing, and it
covers a wide range of tasks involving different phenomena present in human speech. It …

Numerous noise adaptation techniques have been proposed to fine-tune deep-learning
models in speech enhancement (SE) for mismatched noise environments. Nevertheless …

For deep learning-based speech enhancement (SE) systems, the training-test acoustic
mismatch can cause notable performance degradation. To address the mismatch issue …

The incompleteness of speech inputs severely degrades the performance of all the related
speech signal processing applications. Although many researches have been proposed to …

In this report, we present a hybrid neural network system on the task of the 2nd Clarity
Enhancement Challenge. The system, consisting of two stages, handles noisy-reverberant …