Enhanced Speech Detection Models for Air Travel Disease Risk

Authors

Keywords:

Speaker diarization, artificial intelligence, airport, aircraft cabin

Abstract

In noisy environments such as airport terminals or aircraft cabins, travelers tend to speak louder and to draw closer to their interlocutors, increasing the risk of respiratory particle dispersion and contagion. Accurate assessment of this risk is crucial amidst multiple overlapping speech sources. Utilizing advanced signal processing techniques, particularly artificial intelligence-based speaker diarization, this study focuses on accurately determining speaker identities, speech parameters, and sound levels without content disclosure. The paper describes tailored speaker diarization algorithms and presents validation and preliminary results. By analyzing speech features, the algorithms are used to calculate speech duration and sound pressure levels for each speaker and sentence, to assess viral contaminant spread. The article presents the implemented speaker diarization algorithms and the results for noisy, multi-participant environments, as part of a broader project effort aiming at proactively develop disease containment measures in air travel settings.

Additional Files

Published

2024-05-12

How to Cite

1.
KONE TC, Ghinet S, Grewal A, Sayed Ahmed D. Enhanced Speech Detection Models for Air Travel Disease Risk. Canadian Acoustics [Internet]. 2024 May 12 [cited 2024 Nov. 7];52(1). Available from: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/4154

Issue

Vol. 52 No. 1 (2024): Proceedings of Acoustics Week in Canada 2024 Conference joint with 186th Meeting of the Acoustical Society of America

Section

Proceedings of the Acoustics Week in Canada

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