Robust laughter detection for wearable wellbeing sensing

To build a noise-robust online-capable laughter detector for be-havioural monitoring on wearables, we incorporate context-sensitive Long Short-Term Memory Deep Neural Networks. We show our solution’s improvements over a laughter detection baseline by integrating intelligent noise-robust voice activity detection (VAD) into the same model. To this end, we add extensive artificially mixed VAD data without any laughter targets to a small laughter training set. The resulting laughter detection enhancements are stable even when frames are dropped, which happen in low resource environments such as wearables. Thus, the outlined model generation potentially improves the detection of vocal cues when the amount of training data is small and robustness and efficiency are required.