StressSense: Detecting Stress in Unconstrained Acoustic Environments using Smartphones
Year: 2012
Type: article
Abstract: Stress can have long term adverse effects on individuals’ physical and mental well-being. Changes in the speech pro-duction process is one of many physiological changes that happen during stress. Microphones, embedded in mobile phones and carried ubiquitously by people, provide the op-portunity to continuously and non-invasively monitor stress in real-life situations. We propose StressSense for unob-trusively recognizing stress from human voice using smart-phones. We investigate methods for adapting a one-size-fits-all stress model to individual speakers and scenarios. We demonstrate that the StressSense classifier can robustly iden-tify stress across multiple individuals in diverse acoustic en-vironments: using model adaptation StressSense achieves 81 % and 76 % accuracy for indoor and outdoor environments, respectively. We show that StressSense can be implemented on commodity Android phones and run in real-time. To the best of our knowledge, StressSense represents the first system to consider voice based stress detection and model adaptation in diverse real-life conversational situations us-ing smartphones.
Authors: Hong Lu, Denise Frauendorfer, Daniel Gatica-perez, Mashfiqui Rabbi, Marianne Schmid Mast, Gokul T. Chittaranjan, Andrew T. Campbell, Tanzeem Choudhury
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7075632487