Paper: Federated Learning for Speech Emotion Recognition Applications
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.
Figure 1: An illustration of the federated learning in distributed network of clients with SER application.
S. Latif, S. Khalifa, R. Rana and R. Jurdak, “Poster Abstract: Federated Learning for Speech Emotion Recognition Applications,” 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Sydney, Australia, 2020, pp. 341-342, doi: 10.1109/IPSN48710.2020.00-16.
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