Abstract

Speech carries rich information not only about an individual’s intent but about demographic traits, physical and psychological state among other things. Notably, continuously worn wearable sensors enable researchers to collect egocentric speech data to study and assess real-life expressed emotions, offering unprecedented opportunities for applications in the field of assistive agents, medical diagnoses, and personalized education. Many existing systems collect and transmit these speech data, either processed or unprocessed, from users’ devices to a central server for post analysis. However, egocentric audio sensing for speech emotion recognition has created concerns and risks to privacy, where unintended/improper inferences of sensitive information and demographic information may occur without user consent. Toward addressing these concerns, in this work, we propose a privacy-preserving data …

Date

2021

Authors

Tiantian Feng, Shrikanth Narayanan

Conference

2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)

Pages

1-7

Publisher

IEEE