Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

Recent studies in deep learning-based speech separation have proven the superiority of
time-domain approaches to conventional time-frequency-based methods. Unlike the time …

Supervised learning-based methods for source localization, being data driven, can be
adapted to different acoustic conditions via training and have been shown to be robust to …

This paper describes a dataset and protocols for evaluating continuous speech separation
algorithms. Most prior speech separation studies use pre-segmented audio signals, which …

Continuous speech separation was recently proposed to deal with the overlapped speech in
natural conversations. While it was shown to significantly improve the speech recognition …

Complex-valued processing has brought deep learning-based speech enhancement and
signal extraction to a new level. Typically, the process is based on a time-frequency (TF) …

Although advances in close-talk speech recognition have resulted in relatively low error
rates, the recognition performance in far-field environments is still limited due to low signal …

The minimum variance distortionless response (MVDR) beamformer, also known as
Capon's beamformer, is widely studied in the area of speech enhancement. The MVDR …

Noise fields encountered in real-life scenarios can often be approximated as spherical or
cylindrical noise fields. The characteristics of the noise field can be described by a spatial …

This paper presents a time-frequency masking based online multi-channel speech
enhancement approach that uses a convolutional recurrent neural network to estimate the …