Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams
and has become a fast-growing technology thanks to the paradigm shift introduced by deep …

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 …

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 …

The availability of open-source software is playing a remarkable role in the popularization of
speech recognition and deep learning. Kaldi, for instance, is nowadays an established …

Multichannel automatic speech recognition (ASR) systems commonly separate speech
enhancement, including localization, beamforming, and postfiltering, from acoustic …

The machine recognition of speech spoken at a distance from the microphones, known as
far-field automatic speech recognition (ASR), has received a significant increase in attention …

Once a popular theme of futuristic science fiction or far-fetched technology forecasts, digital
home assistants with a spoken language interface have become a ubiquitous commodity …

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

This paper addresses the problem of single channel speech recognition of a target speaker
in a mixture of speech signals. We propose to exploit auxiliary speaker information provided …