Curriculum learning begins to thrive in the speech enhancement area, which decouples the
original spectrum estimation task into multiple easier sub-tasks to achieve better …

Dynamical variational autoencoders (DVAEs) are a class of deep generative models with
latent variables, dedicated to model time series of high-dimensional data. DVAEs can be …

Most of the deep learning-based speech enhancement models are learned in a supervised
manner, which implies that pairs of noisy and clean speech are required during training …

The decoupling-style concept begins to ignite in the speech enhancement area, which
decouples the original complex spectrum estimation task into multiple easier sub-tasks (ie …

Supervised neural speech enhancement methods always require a large scale of paired
noisy and clean speech data. Since collecting adequate paired data from real-world …

Multi-stage or multi-generator generative adversarial networks (GANs) have recently been
demonstrated to be effective for speech enhancement. The existing multi-generator GANs …

The utilization of face masks is an essential healthcare measure, particularly during times of
pandemics, yet it can present challenges in communication in our daily lives. To address this …

This study presents a Pareto optimized flow-based generative network for speech synthesis-
the P-GLOW model in Lithuanian speech synthesis for substituting original voices affected …

Generative Adversarial Networks (GANs) have been used in the field of speech
enhancement due to their huge potentials in reducing the noise mixed in the signals. Most of …

Speech enhancement (SE), which reconstructs intelligible speech by removing noise or
interference from noisy speech, plays an important role in many speech applications. Due to …