In this paper we describe a mobile and wearable devices app that recognises laughter from speech in real-time. The laughter detection is based on a deep neural network architecture, which runs smoothly and robustly, even natively on a smartwatch. Further, this paper presents results demonstrating that our approach achieves state-of-the-art laughter detection performance on the SSPNet Vocalization Corpus (SVC) from the 2013 Interspeech Computational Paralinguistics Challenge Social Signals Sub-Challenge. As this technology is tailored for mobile and wearable devices, it enables and motivates many new use cases, for example, deployment in health care settings such as laughter tracking for psychological coaching, depression monitoring, and therapies.
Cite as: Hagerer, G., Cummins, N., Eyben, F., Schuller, B. (2017) “Did you laugh enough today?” — Deep Neural Networks for Mobile and Wearable Laughter Trackers. Proc. Interspeech 2017, 2044-2045
@inproceedings{hagerer17_interspeech,
author={Gerhard Hagerer and Nicholas Cummins and Florian Eyben and Björn Schuller},
title={{“Did you laugh enough today?” — Deep Neural Networks for Mobile and Wearable Laughter Trackers}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={2044--2045}
}