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End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks by Grósz, Tamás; Singh, Mittul; Kadiri, Sudarsana Reddy; Kathania, Hemant; Kurimo, Mikko is a scholarly article available to read on EtoBox.

What is End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks about?

The events of recent years have highlighted the importance of telemedicine solutions which could potentially allow remote treatment and diagnosis. Relatedly, Computational Paralinguistics, a unique subfield of Speech Processing, aims to extract information about the speaker and form an important part of telemedicine applications. In this work, we focus on two paralinguistic problems: mask detection and breathing state prediction. Solutions developed for these tasks could be invaluable and have the potential to help monitor and limit the spread of a virus like COVID-19. The current state-of-the-art methods proposed for these tasks are ensembles based on deep neural networks like ResNets in conjunction with feature engineering. Although these ensembles can achieve high accuracy, they also have a large footprint and require substantial computational power reducing portability to devices with limited resources. These drawbacks also mean that the previously proposed solutions are infeasible to be used in a telemedicine system due to their size and speed. On the other hand, employing lighter feature-engineered systems can be laborious and add further complexity making them difficult to c

Author

Grósz, Tamás; Singh, Mittul; Kadiri, Sudarsana Reddy; Kathania, Hemant; Kurimo, Mikko

Published

2022

Language

EN