Automatic development of speech-in-noise hearing tests using machine learning
Abstract Understanding 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 the...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96312-z |
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| author | Sigrid Polspoel David R. Moore De Wet Swanepoel Sophia E. Kramer Cas Smits |
| author_facet | Sigrid Polspoel David R. Moore De Wet Swanepoel Sophia E. Kramer Cas Smits |
| author_sort | Sigrid Polspoel |
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| description | Abstract Understanding 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. |
| format | Article |
| id | doaj-art-ce2ea97d136f44a6a3a1603b6174502c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-ce2ea97d136f44a6a3a1603b6174502c2025-08-20T02:27:53ZengNature PortfolioScientific Reports2045-23222025-04-0115112010.1038/s41598-025-96312-zAutomatic development of speech-in-noise hearing tests using machine learningSigrid Polspoel0David R. Moore1De Wet Swanepoel2Sophia E. Kramer3Cas Smits4Otolaryngology-Head and Neck Surgery, Section Ear and Hearing, Amsterdam UMC location Vrije Universiteit AmsterdamDivision of Patient Services Research, Cincinnati Children’s Hospital Medical Center, and Department of Pediatrics, University of CincinnatiDepartment of Speech-Language Pathology and Audiology, University of PretoriaOtolaryngology-Head and Neck Surgery, Section Ear and Hearing, Amsterdam UMC location Vrije Universiteit AmsterdamAmsterdam Public Health research institute, Quality of CareAbstract Understanding 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.https://doi.org/10.1038/s41598-025-96312-zArtificial intelligence (AI)Synthetic speechText-to-speech (TTS)Automatic speech recognition (ASR)AladdinDigits-in-noise test |
| spellingShingle | Sigrid Polspoel David R. Moore De Wet Swanepoel Sophia E. Kramer Cas Smits Automatic development of speech-in-noise hearing tests using machine learning Scientific Reports Artificial intelligence (AI) Synthetic speech Text-to-speech (TTS) Automatic speech recognition (ASR) Aladdin Digits-in-noise test |
| title | Automatic development of speech-in-noise hearing tests using machine learning |
| title_full | Automatic development of speech-in-noise hearing tests using machine learning |
| title_fullStr | Automatic development of speech-in-noise hearing tests using machine learning |
| title_full_unstemmed | Automatic development of speech-in-noise hearing tests using machine learning |
| title_short | Automatic development of speech-in-noise hearing tests using machine learning |
| title_sort | automatic development of speech in noise hearing tests using machine learning |
| topic | Artificial intelligence (AI) Synthetic speech Text-to-speech (TTS) Automatic speech recognition (ASR) Aladdin Digits-in-noise test |
| url | https://doi.org/10.1038/s41598-025-96312-z |
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