Speech separation algorithms are often used to separate the target speech from other
interfering sources. However, purely neural network based speech separation systems often …

Abstract Automatic Speech Recognition (ASR) system is an emerging technology used in
various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of …

Speech enhancement systems, which denoise and dereverberate distorted signals, are
usually optimized based on signal reconstruction objectives including the maximum …

Deep neural network (DNN) embeddings for speaker recognition have recently attracted
much attention. Compared to i-vectors, they are more robust to noise and room …

Accurate recognition of cocktail party speech containing overlapping speakers, noise and
reverberation remains a highly challenging task to date. Motivated by the invariance of …

Far-field multi-speaker automatic speech recognition (ASR) has drawn increasing attention
in recent years. Most existing methods feature a signal processing frontend and an ASR …

Recent work has shown that it is feasible to use generative adversarial networks (GANs) for
speech enhancement, however, these approaches have not been compared to state-of-the …

Many purely neural network based speech separation approaches have been proposed to
improve objective assessment scores, but they often introduce nonlinear distortions that are …

Existing generative adversarial networks (GANs) for speech enhancement solely rely on the
convolution operation, which may obscure temporal dependencies across the sequence …

Purely neural network (NN) based speech separation and enhancement methods, although
can achieve good objective scores, inevitably cause nonlinear speech distortions that are …