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 …

We present a new method for separating a mixed audio sequence, in which multiple voices
speak simultaneously. The new method employs gated neural networks that are trained to …

Speaker extraction aims to mimic humans' selective auditory attention by extracting a target
speaker's voice from a multi-talker environment. It is common to perform the extraction in …

An important problem in ad-hoc microphone speech separation is how to guarantee the
robustness of a system with respect to the locations and numbers of microphones. The …

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

Beamforming has been extensively investigated for multi-channel audio processing tasks.
Recently, learning-based beamforming methods, sometimes called neural beamformers …

This paper proposes a unified architecture for end-to-end automatic speech recognition
(ASR) to encompass microphone-array signal processing such as a state-of-the-art neural …

The field of speech recognition is in the midst of a paradigm shift: end-to-end neural
networks are challenging the dominance of hidden Markov models as a core technology …

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 …

Speaker extraction is to extract a target speaker's voice from multi-talker speech. It simulates
humans' cocktail party effect or the selective listening ability. The prior work mostly performs …