VADLite: An Open-Source Lightweight System for Real-Time Voice Activity Detection on Smartwatches
Type
conference paper
Date Issued
2019-09-09
Author(s)
Abstract (De)
Smartwatches provide a unique opportunity to collect more speech data because they are always with the user and also have a more exposed microphone compared to smartphones. Speech data could be used to infer various indicators of mental well being such as emotions, stress and social activity. Hence, real-time voice activity detection (VAD) on smart- watches could enable the development of applications for mental health monitoring. In this work, we present VADLite, an open-source, lightweight, system that performs real-time VAD on smartwatches. It extracts mel-frequency cepstral coefficients and classifies speech versus non-speech audio samples using a linear Support Vector Machine. The real-time implementation is done on the Wear OS Polar M600 smartwatch. An offline and online evaluation of VADLite using real-world data showed better performance than WebRTC’s open-source VAD system. VADLite can be easily integrated into Wear OS projects that need a lightweight VAD module running on a smartwatch.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Book title
4th International Workshop on Mental Health: Sensing & Intervention, co-located with the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
Publisher
ACM
Publisher place
London, UK
Event Title
4th International Workshop on Mental Health: Sensing & Intervention, co-located with the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)
Event Location
London, UK
Event Date
September 9-13, 2019
Division(s)
Eprints ID
258041
File(s)![Thumbnail Image]()
Name
Boateng et al 2019 - DyMand - VAD Light.pdf
Size
911.92 KB
Format
Adobe PDF
Checksum (MD5)
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