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

This paper presents a novel discriminator-constrained optimal transport network (DOTN) that
performs unsupervised domain adaptation for speech enhancement (SE), which is an …

The performance of acoustic models degrades notably in noisy environments. Speech
enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition …

The performance of speaker verification (SV) models may drop dramatically in noisy
environments. A speech enhancement (SE) module can be used as a front-end strategy …

This paper presents a novel self-adaptive approach for speech enhancement in the context
of highly nonstationary noise. A two-stage deep neuroevolutionary technique for speech …

Deep learning based speech enhancement (SE) approaches could suffer from performance
degradation due to mismatch between training and testing environments. A realistic situation …

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 …

This study presents a deep learning-based speech signal-processing mobile application
known as CITISEN. The CITISEN can perform three functions: speech enhancement (SE) …