Federated Learning for Speech Emotion Recognition Applications

Poster

Latif, Siddique, Khalifa, Sara, Rana, Rajib and Jurdak, Raja. 2020. "Federated Learning for Speech Emotion Recognition Applications." 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2020). Sydney, Australia 21 - 24 Apr 2020 United States. https://doi.org/10.1109/IPSN48710.2020.00-16
Paper/Presentation Title

Federated Learning for Speech Emotion Recognition Applications

Presentation TypePoster
AuthorsLatif, Siddique (Author), Khalifa, Sara (Author), Rana, Rajib (Author) and Jurdak, Raja (Author)
Journal or Proceedings Title2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
Number of Pages2
Year2020
Place of PublicationUnited States
ISBN9781728154978
Digital Object Identifier (DOI)https://doi.org/10.1109/IPSN48710.2020.00-16
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9111050
Conference/Event19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2020)
Event Details
Rank
A
A
A
A
A
A
Event Details
19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2020)
Event Date
21 to end of 24 Apr 2020
Event Location
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.

KeywordsFederated learning, deep neural networks, privacy preserving, speech emotion recognition
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460212. Speech recognition
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Byline AffiliationsInstitute for Resilient Regions
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
School of Sciences
Queensland University of Technology
Institution of OriginUniversity of Southern Queensland
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