Polspoel Sigrid, Moore David R, Swanepoel De Wet, Kramer Sophia E, Smits Cas
Otolaryngology-Head and Neck Surgery, Section Ear and Hearing, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands.
Amsterdam Public Health research institute, Quality of Care, Amsterdam, The Netherlands.
Sci Rep. 2025 Apr 15;15(1):12878. doi: 10.1038/s41598-025-96312-z.
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.
在嘈杂环境中理解言语是听力损失患者面临的主要挑战，这会影响日常交流和生活质量。传统的噪声环境下言语测试对于听力损失的筛查和诊断至关重要，但开发此类测试资源消耗大，这使得它们在低收入和中等收入国家难以普及。本研究引入了一种基于人工智能的方法来自动化这些测试的开发。通过利用文本转语音和自动语音识别（ASR）技术，可以降低高质量噪声环境下言语测试所需的成本、时间和资源。该程序名为“阿拉丁”（数字噪声测试的自动语言无关开发），通过合成语音材料创建数字噪声（DIN）听力测试，并使用基于ASR的电平校正来使数字在感知上达到均衡。将传统的DIN测试与新开发的荷兰语和英语阿拉丁测试在听力正常和听力损失的受试者中进行比较。阿拉丁测试显示出84%的特异性和100%的敏感性，与参考DIN测试（87%和100%）相似。阿拉丁为跨语言开发DIN测试提供了通用指南，解决了比较不同版本测试结果的挑战。阿拉丁的方法代表了测试开发的重大进步，并为全球听力损失的筛查和治疗提供了有效的改进。
