The use of deep learning (DL) architectures for speech enhancement has recently improved
the robustness of voice applications under diverse noise conditions. These improvements …

Speech enhancement systems aim to improve the quality and intelligibility of noisy speech.
In this study, we compare two speech enhancement systems based on deep neural …

Deep learning based speech enhancement has made rapid development towards
improving quality, while models are becoming more compact and usable for real-time on-the …

We propose three algorithms to address the mismatch problem in deep neural network
(DNN) based speech enhancement. First, we investigate noise aware training by …

A speech emotion recognition (SER) system deployed on a real-world application can
encounter speech contaminated with unconstrained background noise. To deal with this …

Speech enhancement using neural networks is recently receiving large attention in research
and being integrated in commercial devices and applications. In this work, we investigate …

When speech emotion recognition (SER) is applied in an actual application, the system
should be able to cope with audio acquired in a noisy, unconstrained environment. Most …

The field of automatic speech recognition has evolved greatly since the introduction of deep
learning, which began only about 5 years ago. In particular, as more and more products …

In this paper, we propose a novel teacher-student learning framework for the preprocessing
of a speech recognizer, leveraging the online noise tracking capabilities of improved minima …

In contrast to the conventional minimum mean square error (MMSE)-based noise reduction
techniques, we propose a supervised method to enhance speech by means of finding a …