Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …

Speech enhancement has benefited from the success of deep learning in terms of
intelligibility and perceptual quality. Conventional time-frequency (TF) domain methods …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

The synthesis of natural emotional reactions is an essential criterion in vivid talking-face
video generation. This criterion is nevertheless seldom taken into consideration in previous …

Time-frequency (TF) domain masking is a mainstream approach for single-channel speech
enhancement. Recently, focuses have been put to phase prediction in addition to amplitude …

Adversarial loss in a conditional generative adversarial network (GAN) is not designed to
directly optimize evaluation metrics of a target task, and thus, may not always guide the …

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 …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

Separation of speech embedded in non-stationary interference is a challenging problem that
has recently seen dramatic improvements using deep network-based methods. Previous …