Vadlite: An open-source lightweight system for real-time voice activity detection on smartwatches

Abstract

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 smartwatches 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.