AI classification of respiratory illness through vocal biomarkers and a bespoke articulatory speech protocol

Tim Bashford*, Hok Shing Lau, Mark Huntly, Nathan Morgan, Adesua Iyenoma, Tom Powell, Biao Zeng

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl Cynhadleddadolygiad gan gymheiriaid

53 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Speech biomarkers represent a powerful indicator for detecting, monitoring and categorising neurological, psychological , pathological and pulmonary conditions. Facilitated by advances in computational power and artificial intelligence (AI) techniques, we present a novel ecosystem for data acquisition , analysis and storage, using an articulatory speech task. By automatically segmenting, aligning and extracting features from the vocal recordings, we present a feature extraction pipeline toward the classification of pathological conditions, specifically respiratory disease through recorded voice. Data is stored within a Trusted Research Environment, for which this work also presents a range of ethical considerations.
Iaith wreiddiolSaesneg
Rhif yr erthygl13
Nifer y tudalennau5
CyfnodolynInternational Journal of Simulation, Systems, Science and Technology
Cyfrol25
Rhif cyhoeddi1
StatwsCyhoeddwyd - Maw 2024
DigwyddiadUKSim-AMSS: International Conference on Mathematical/Analytical Modelling and Computer Simulation - Emmanuel College, Cambridge, Y Deyrnas Unedig
Hyd: 26 Maw 202428 Maw 2024
Rhif y gynhadledd: 26th

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