Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning

• URL: http://arxiv.org/abs/2202.02611v1
• Date: Sat, 5 Feb 2022 18:30:23 GMT
• Title: Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning
• Authors: Vasileios Tsouvalas, Tanir Ozcelebi, Nirvana Meratnia
• Abstract summary: Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech. Existing SER approaches are largely centralized, without considering users' privacy. We present a privacy-preserving and data-efficient SER approach by utilizing the concept of Federated Learning.
• Score: 0.8508198765617195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech. If done accurately, it can offer a number of benefits in building human-centered context-aware intelligent systems. Existing SER approaches are largely centralized, without considering users' privacy. Federated Learning (FL) is a distributed machine learning paradigm dealing with decentralization of privacy-sensitive personal data. In this paper, we present a privacy-preserving and data-efficient SER approach by utilizing the concept of FL. To the best of our knowledge, this is the first federated SER approach, which utilizes self-training learning in conjunction with federated learning to exploit both labeled and unlabeled on-device data. Our experimental evaluations on the IEMOCAP dataset shows that our federated approach can learn generalizable SER models even under low availability of data labels and highly non-i.i.d. distributions. We show that our approach with as few as 10% labeled data, on average, can improve the recognition rate by 8.67% compared to the fully-supervised federated counterparts.

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