Paper Title :Speech Emotion Recognition Through Federated Learning for Quality Assurance in Call Centers

Author :Kristal Joanne D’souza, Ann Kurian, Andre Aditya Roy, Arshan Lawrence Rodrigues, Pranjali Thakre

Article Citation :Kristal Joanne D’souza ,Ann Kurian ,Andre Aditya Roy ,Arshan Lawrence Rodrigues ,Pranjali Thakre , (2024 ) " Speech Emotion Recognition Through Federated Learning for Quality Assurance in Call Centers " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 74-81, Volume-12,Issue-2

Abstract : For call centers, emotion recognition (ER) is a helpful application since it helps prevent customer abuse, match clients with qualified agents, provide agent support and training, and raise client happiness. Federated learning makes it possible to train an extremely accurate ER model while protecting privacy. FL makes sure that private information is kept on local servers and devices and isn't shared with a centralized server. We propose a thorough approach to emotion analysis in our project, which uses a dual encoder architecture made up of a CNN and a Bi-LSTM network. The call center management can then receive an analytical report showing the agent's emotion trend and utilize the trained ER model to categorize the emotions of their customers. Our method shows 88% accuracy on training data and 82% accuracy on test data in recognizing and comprehending emotional subtleties in customer-agent discussions. Furthermore, as it can assist in identifying areas where agents require additional training or support, ER through FL can also be utilized for quality assurance. In conclusion, ER via FL is a potential method for leveraging machine learning in a privacy-preserving manner to improve the client experience in call centers. Keywords - Federated learning, neural networks, call centers, privacy-preserving, speech emotion recognition, quality assurance, dual encoder

Type : Research paper

Published : Volume-12,Issue-2

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