Privacy Risks in Speech Emotion Recognition: A Systematic Study on Gender Inference Attack

Alsenani, B., Guha, T. and Vinciarelli, A. (2023) Privacy Risks in Speech Emotion Recognition: A Systematic Study on Gender Inference Attack. In: 24th INTERSPEECH Conference, Dublin, Ireland, 20-24 Aug 2023, pp. 651-655. (doi: 10.21437/Interspeech.2023-454)

Abstract

Increasingly more applications now use deep networks to analyse speaker's affective states. An undesirable side effect is that models trained to perform one task (e.g, emotion from speech) can be attacked to infer other, possibly privacy-sensitive attributes (e.g., gender) of the speaker. The amount of information an attacker can infer through such attacks is called leakage, and this article presents the first systematic study of the interplay between gender leakage and the main characteristics of the attacker model (family, architecture and training condition). To this end, we define various attack scenarios, and perform extensive experiments to analyse privacy risks in Speech Emotion Recognition (SER). Results show that SER models can leak a speaker's gender with an accuracy of 51% to 95% (upper bound) depending on the attack condition. Furthermore, our results provide fresh insights on how to limit the effectiveness of possible attacks and, thereby, to ensure privacy preservation.

• Item Type: Conference Proceedings
• Additional Information: This research was supported by UKRI grant EP/S02266X/1.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Guha, Dr Tanaya and Alsenani, Miss Basmah and Vinciarelli, Professor Alessandro
• Authors: Alsenani, B., Guha, T., and Vinciarelli, A.
• College/School: College of Science and Engineering > School of Computing Science
• Copyright Holders: Copyright © INTERSPEECH 2023
• First Published: First published in Proc. INTERSPEECH 2023: 651-655
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
303764		EPSRC CDT - Socially Intelligent Artificial Agents	Alessandro Vinciarelli	Engineering and Physical Sciences Research Council (EPSRC)	EP/S02266X/1	Computing Science