Federated Learning for Speech Emotion Recognition Applications
Poster
Paper/Presentation Title | Federated Learning for Speech Emotion Recognition Applications |
---|---|
Presentation Type | Poster |
Authors | Latif, Siddique (Author), Khalifa, Sara (Author), Rana, Rajib (Author) and Jurdak, Raja (Author) |
Journal or Proceedings Title | 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) |
Number of Pages | 2 |
Year | 2020 |
Place of Publication | United States |
ISBN | 9781728154978 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/IPSN48710.2020.00-16 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/9111050 |
Conference/Event | 19th 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. |
Keywords | Federated learning, deep neural networks, privacy preserving, speech emotion recognition |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460212. Speech recognition |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Institute for Resilient Regions |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
School of Sciences | |
Queensland University of Technology | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5qz3/federated-learning-for-speech-emotion-recognition-applications
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