1 research outputs found
Far-Field Automatic Speech Recognition
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 of attention in science and industry, which caused or was
caused by an equally significant improvement in recognition accuracy. Meanwhile
it has entered the consumer market with digital home assistants with a spoken
language interface being its most prominent application. Speech recorded at a
distance is affected by various acoustic distortions and, consequently, quite
different processing pipelines have emerged compared to ASR for close-talk
speech. A signal enhancement front-end for dereverberation, source separation
and acoustic beamforming is employed to clean up the speech, and the back-end
ASR engine is robustified by multi-condition training and adaptation. We will
also describe the so-called end-to-end approach to ASR, which is a new
promising architecture that has recently been extended to the far-field
scenario. This tutorial article gives an account of the algorithms used to
enable accurate speech recognition from a distance, and it will be seen that,
although deep learning has a significant share in the technological
breakthroughs, a clever combination with traditional signal processing can lead
to surprisingly effective solutions.Comment: accepted for Proceedings of the IEE