Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques

R. Haeb-Umbach, S. Watanabe, T. Nakatani, M. Bacchiani, B. Hoffmeister, M.L. Seltzer, H. Zen, M. Souden, IEEE Signal Processing Magazine 36 (2019) 111–124.

Download
OA 1.09 MB
Journal Article | English
Author
; ; ; ; ; ; ;
Abstract
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 today. This success has been made possible by major advancements in signal processing and machine learning for so-called far-field speech recognition, where the commands are spoken at a distance from the sound capturing device. The challenges encountered are quite unique and different from many other use cases of automatic speech recognition. The purpose of this tutorial article is to describe, in a way amenable to the non-specialist, the key speech processing algorithms that enable reliable fully hands-free speech interaction with digital home assistants. These technologies include multi-channel acoustic echo cancellation, microphone array processing and dereverberation techniques for signal enhancement, reliable wake-up word and end-of-interaction detection, high-quality speech synthesis, as well as sophisticated statistical models for speech and language, learned from large amounts of heterogeneous training data. In all these fields, deep learning has occupied a critical role.
Publishing Year
Journal Title
IEEE Signal Processing Magazine
Volume
36
Issue
6
Page
111-124
ISSN
LibreCat-ID

Cite this

Haeb-Umbach R, Watanabe S, Nakatani T, et al. Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques. IEEE Signal Processing Magazine. 2019;36(6):111-124. doi:10.1109/MSP.2019.2918706
Haeb-Umbach, R., Watanabe, S., Nakatani, T., Bacchiani, M., Hoffmeister, B., Seltzer, M. L., … Souden, M. (2019). Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques. IEEE Signal Processing Magazine, 36(6), 111–124. https://doi.org/10.1109/MSP.2019.2918706
@article{Haeb-Umbach_Watanabe_Nakatani_Bacchiani_Hoffmeister_Seltzer_Zen_Souden_2019, title={Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques}, volume={36}, DOI={10.1109/MSP.2019.2918706}, number={6}, journal={IEEE Signal Processing Magazine}, author={Haeb-Umbach, Reinhold and Watanabe, Shinji and Nakatani, Tomohiro and Bacchiani, Michiel and Hoffmeister, Bjoern and Seltzer, Michael L. and Zen, Heiga and Souden, Mehrez}, year={2019}, pages={111–124} }
Haeb-Umbach, Reinhold, Shinji Watanabe, Tomohiro Nakatani, Michiel Bacchiani, Bjoern Hoffmeister, Michael L. Seltzer, Heiga Zen, and Mehrez Souden. “Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques.” IEEE Signal Processing Magazine 36, no. 6 (2019): 111–24. https://doi.org/10.1109/MSP.2019.2918706.
R. Haeb-Umbach et al., “Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques,” IEEE Signal Processing Magazine, vol. 36, no. 6, pp. 111–124, 2019.
Haeb-Umbach, Reinhold, et al. “Speech Processing for Digital Home Assistance: Combining Signal Processing With Deep-Learning Techniques.” IEEE Signal Processing Magazine, vol. 36, no. 6, 2019, pp. 111–24, doi:10.1109/MSP.2019.2918706.
All files available under the following license(s):
Creative Commons License:
CC0Creative Commons Public Domain Dedication (CC0 1.0)
Main File(s)
Access Level
OA Open Access
Last Uploaded
2020-02-06T07:28:26Z


Export

Marked Publications

Open Data LibreCat

Search this title in

Google Scholar