Title
Speech Processing for Digital Home Assistants: Combining signal processing with deep-learning techniques
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 (ASR). The purpose of this article is to describe, in a way that is amenable to the nonspecialist, the key speech processing algorithms that enable reliable, fully hands-free speech interaction with digital home assistants. These technologies include multichannel acoustic echo cancellation (MAEC), microphone array processing and dereverberation techniques for signal enhancement, reliable wake-up word and end-of-interaction detection, and high-quality speech synthesis as well as sophisticated statistical models for speech and language, learned from large amounts of heterogeneous training data. In all of these fields, deep learning (DL) has played a critical role.
Year
DOI
Venue
2019
10.1109/MSP.2019.2918706
IEEE Signal Processing Magazine
Keywords
Field
DocType
Microphones, Speech recognition, Speech processing, Loudspeakers, Reverberation
Speech processing,Computer vision,Signal processing,Speech synthesis,Use case,Reverberation,Computer science,Speech recognition,Artificial intelligence,Deep learning,Loudspeaker,Spoken language
Journal
Volume
Issue
ISSN
36
6
1053-5888
Citations 
PageRank 
References 
12
0.67
0
Authors
8
Name
Order
Citations
PageRank
Reinhold Haeb-Umbach11487211.71
Shinji Watanabe21158139.38
Tomohiro Nakatani31327139.18
Michiel Bacchiani462155.46
Bjorn Hoffmeister5120.67
Michael L. Seltzer6102769.42
Heiga Zen71922103.73
Mehrez Souden819514.68