Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing

Paper


Ali, Hafiz Shehbaz, Hassan, Fakhar ul, Latif, Siddique, Manzoor, Habib Ullah and Qadir, Junaid. 2021. "Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing." IEEE International Conference on Communications Workshops (2021). Montreal, Canada 14 - 23 Jun 2021 United States. https://doi.org/10.1109/ICCWorkshops50388.2021.9473669
Paper/Presentation Title

Privacy Enhanced Speech Emotion Communication using Deep Learning Aided Edge Computing

Presentation TypePaper
AuthorsAli, Hafiz Shehbaz (Author), Hassan, Fakhar ul (Author), Latif, Siddique (Author), Manzoor, Habib Ullah (Author) and Qadir, Junaid (Author)
Journal or Proceedings Title2021 IEEE International Conference on Communications Workshops (ICC Workshops) Proceedings
ERA Conference ID42928
Number of Pages5
Year2021
Place of PublicationUnited States
ISBN9781728194417
Digital Object Identifier (DOI)https://doi.org/10.1109/ICCWorkshops50388.2021.9473669
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9473669
Conference/EventIEEE International Conference on Communications Workshops (2021)
IEEE International Conference on Communications
Event Details
IEEE International Conference on Communications
ICC
Rank
B
B
B
B
B
B
B
B
B
B
Event Details
IEEE International Conference on Communications Workshops (2021)
Event Date
14 to end of 23 Jun 2021
Event Location
Montreal, Canada
Abstract

Speech emotion sensing in communication networks has a wide range of applications in real life. In these applications, voice data are transmitted from the user to the central server for storage, processing, and decision making. However, speech data contain vulnerable information that can be used maliciously without the user's consent by an eavesdropping adversary. In this work, we present a privacy-enhanced emotion communication system for preserving the user personal information in emotion-sensing applications. We propose the use of an adversarial learning framework that can be deployed at the edge to unlearn the users' private information in the speech representations. These privacy-enhanced representations can be transmitted to the central server for decision making. We evaluate the proposed model on multiple speech emotion datasets and show that the proposed model can hide users' specific demographic information and improve the robustness of emotion identification without significantly impacting performance. To the best of our knowledge, this is the first work on a privacy-preserving framework for emotion sensing in the communication network.

Keywordsemotion communication system, speech emotionrecognition, privacy enhanced features, deep learning, edgecomputing.
ANZSRC Field of Research 2020461101. Adversarial machine learning
461106. Semi- and unsupervised learning
461103. Deep learning
461104. Neural networks
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Byline AffiliationsEmulation AI, Australia
Information Technology University, Pakistan
University of Southern Queensland
University of Engineering and Technology, Pakistan
Institution of OriginUniversity of Southern Queensland
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