Federated Learning for Speech Emotion Recognition Applications

Latif, Siddique and Khalifa, Sara and Rana, Rajib and Jurdak, Raja (2020) Federated Learning for Speech Emotion Recognition Applications. In: 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2020), 21-24 April, 2020, Sydney, Australia.


Abstract

Privacy concerns are considered one of the major challenges in the applications of speech emotion recognition (SER) as it involves the complete sharing of speech data, which can bring threatening consequences to people’s lives. Federated learning is an effective technique to avoid privacy infringement by involving multiple participants to collaboratively learn a shared model without revealing their local data. In this work, we evaluated federated learning for SER using a publicly available dataset. Our preliminary results show that speech emotion recognition can benefit from federated learning by not exporting sensitive user data to central servers, while achieving promising results compared to the state-of-the-art.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Poster)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions
Date Deposited: 20 Aug 2020 04:58
Last Modified: 13 Oct 2020 05:00
Uncontrolled Keywords: Federated learning, deep neural networks, privacy preserving, speech emotion recognition
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
Identification Number or DOI: 10.1109/IPSN48710.2020.00-16
URI: http://eprints.usq.edu.au/id/eprint/38551

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