Speech enhancement in the time domain is becoming increasingly popular in recent years,
due to its capability to jointly enhance both the magnitude and the phase of speech. In this …

Recently, Transformer shows the potential to exploit the long-range sequence dependency
in speech with self-attention. It has been introduced in single channel speech enhancement …

In this work, we exploit speech enhancement for improving a re-current neural network
transducer (RNN-T) based ASR system. We employ a dense convolutional recurrent …

The acoustic variability of noisy and reverberant speech mixtures is influenced by multiple
factors, such as the spectro-temporal characteristics of the target speaker and the interfering …

Most of the current approaches for speech enhancement (SE) using deep neural network
(DNN) face a number of limitations: they do not exploit information contained in the phase …

Deep learning has led to unprecedented advances in speech enhancement. However, deep
neural networks (DNNs) typically require large amount of computation, memory, signal …

Deep neural networks (DNNs) represent the mainstream methodology for supervised
speech enhancement, primarily due to their capability to model complex functions using …

Recent years have brought considerable advances to our ability to increase intelligibility
through deep-learning-based noise reduction, especially for hearing-impaired (HI) listeners …

Dealing with speech interference in a speech enhancement system requires either speaker
separation or target speaker extraction. Speaker separation has multiple output streams with …

We propose a dual-path self-attention recurrent neural network (DP-SARNN) for time-
domain speech enhancement. We improve dual-path RNN (DP-RNN) by augmenting inter …