Federated learning based privacy preserved English accent training ecosystem for people with Indian language accent

Abstract

Language refers to the system of communication used by a community, while accent refers to the distinct way in which an individual or group pronounces words or phrases within that language. This paper aims to design and develop a privacy-preserving federated learning-based intervention for individuals to identify a recommending system for the improvement of English accents. In methodology, the deep learning algorithm is trained and validated using the AccentDB dataset and further same model is implemented over Federated Learning Ecosystem. Data in terms of identical and independent distribution is analyzed to ensure the productivity of the trained model in data constraint environments. The results section is focused to analyse the utilization of proposed implementations. Further, the trained model is applied to mobile and web applications for accessing levels and then training English accents to individuals. Similarly, to analyze incremental learning through federated learning, the implementation is analyzed on basis of parameters viz., accuracy, precision, recall, and loss. In the future, this proposed system could be embedded with various other applications for the overall enhancement of effective communication skills.