Name
Abstract | ||
|---|---|---|
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 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 with 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 multicondition 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JPROC.2020.3018668 | Proceedings of the IEEE |
Keywords | DocType | Volume |
Acoustic beamforming,automatic speech recognition (ASR),dereverberation,end-to-end speech recognition,speech enhancement | Journal | 109 |
Issue | ISSN | Citations |
2 | 0018-9219 | 8 |
PageRank | References | Authors |
0.60 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reinhold Haeb-Umbach | 1 | 1487 | 211.71 |
| Jahn Heymann | 2 | 102 | 10.29 |
| Lukas Drude | 3 | 95 | 11.10 |
| Shinji Watanabe | 4 | 1158 | 139.38 |
| Marc Delcroix | 5 | 699 | 62.07 |
| Tomohiro Nakatani | 6 | 1327 | 139.18 |