Abstract
Towards implementation of adult hearing screening tests that can be delivered via a mobile app, we have recently designed a novel speech-in-noise test based on the following requirements: user-operated, fast, reliable, accurate, viable for use by listeners of unknown native language and viable for testing at a distance. This study addresses specific models to (i) investigate the ability of the test to identify ears with mild hearing loss using machine learning; and (ii) address the range of the output levels generated using different transducers. Our results demonstrate that the test classification performance using decision tree models is in line with the performance of validated, language-dependent speech-in-noise tests. We observed, on average, 0.75 accuracy, 0.64 sensitivity and 0.81 specificity. Regarding the analysis of output levels, we demonstrated substantial variability of transducers’ characteristics and dynamic range, with headphones yielding higher output levels compared to earphones. These findings confirm the importance of a self-adjusted volume option. These results also suggest that earphones may not be suitable for test execution as the output levels may be relatively low, particularly for subjects with hearing loss or for those who skip the volume adjustment step. Further research is needed to fully address test performance, e.g. testing a larger sample of subjects, addressing different classification approaches, and characterizing test reliability in varying conditions using different devices and transducers.
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Acknowledgement
The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program or ERC Consolidator Grant: SONORA (773268). This article reflects only the authors’ views, and the Union is not liable for any use that may be made of the contained information.
The authors are grateful to the Lions Clubs International and to Associazione La Rotonda, Baranzate (MI) for their support in the organization and management of experiments in the unscreened population of adults. The authors wish to thank Anna Bersani, Carola Butera, and Antonio Carrella from Politecnico di Milano who helped with data collection at Associazione La Rotonda. The Authors would also like to thank Dr. Randall Ali from the Department of Electrical Engineering at KU Leuven for providing guidance during the transducers characterization experiment.
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Polo, E.M., Zanet, M., Lenatti, M., van Waterschoot, T., Barbieri, R., Paglialonga, A. (2021). Development and Evaluation of a Novel Method for Adult Hearing Screening: Towards a Dedicated Smartphone App. In: Goleva, R., Garcia, N.R.d.C., Pires, I.M. (eds) IoT Technologies for HealthCare. HealthyIoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-030-69963-5_1
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