Automatic development of speech-in-noise hearing tests using machine learning

dc.contributor.authorPolspoel, Sigrid
dc.contributor.authorMoore, David R.
dc.contributor.authorSwanepoel, De Wet
dc.contributor.authorKramer, Sophia E.
dc.contributor.authorSmits, Cas
dc.date.accessioned2025-05-16T10:20:40Z
dc.date.available2025-05-16T10:20:40Z
dc.date.issued2025-04
dc.descriptionDATA AVAILABILITY : The data that support the findings of this study are available from the corresponding author upon reasonable request.
dc.description.abstractUnderstanding speech in noisy environments is a primary challenge for individuals with hearing loss, affecting daily communication and quality of life. Traditional speech-in-noise tests are essential for screening and diagnosing hearing loss but are resource-intensive to develop, making them less accessible in low and middle-income countries. This study introduces an artificial intelligence-based approach to automate the development of these tests. By leveraging text-to-speech and automatic speech recognition (ASR) technologies, the cost, time, and resources required for high-quality speech-in-noise testing could be reduced. The procedure, named “Aladdin” (Automatic LAnguage-independent Development of the digits-in-noise test), creates digits-in-noise (DIN) hearing tests through synthetic speech material and uses ASR-based level corrections to perceptually equalize the digits. Traditional DIN tests were compared with newly developed Dutch and English Aladdin tests in listeners with normal hearing and hearing loss. Aladdin tests showed 84% specificity and 100% sensitivity, similar to the reference DIN tests (87% and 100%). Aladdin provides a universal guideline for developing DIN tests across languages, addressing the challenge of comparing test results across variants. Aladdin’s approach represents a significant advancement in test development and offers an efficient enhancement to global screening and treatment for hearing loss.
dc.description.departmentSpeech-Language Pathology and Audiology
dc.description.librarianhj2025
dc.description.sdgSDG-03: Good health and well-being
dc.description.sponsorshipThe NIHR Manchester Biomedical Research Centre and PPP Allowance.
dc.description.urihttps://www.nature.com/srep
dc.identifier.citationPolspoel, S., Moore, D.R., Swanepoel, D.W. et al. Automatic development of speech-in-noise hearing tests using machine learning. Scientific Reports 15, 12878 (2025). https://doi.org/10.1038/s41598-025-96312-z.
dc.identifier.issn2045-2322 (online)
dc.identifier.other10.1038/s41598-025-96312-z
dc.identifier.urihttp://hdl.handle.net/2263/102418
dc.language.isoen
dc.publisherNature Research
dc.rights© The Author(s) 2025. Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
dc.subjectAutomatic speech recognition (ASR)
dc.subjectDigits-in-noise (DIN)
dc.subjectArtificial intelligence (AI)
dc.subjectSynthetic speech
dc.subjectText-to-speech (TTS)
dc.subjectAladdin
dc.subjectDigits-in-noise test
dc.titleAutomatic development of speech-in-noise hearing tests using machine learning
dc.typeArticle

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