Key points are not available for this paper at this time.
ABSTRACT Understanding speech in noisy settings is one of the biggest challenges for individuals with hearing loss. Traditional speech-in-noise tests play a crucial role in screening for and diagnosing hearing loss, but are resource-intensive to develop, limiting accessibility, particularly in low and middle-income countries. This four-part study introduces an innovative approach using artificial intelligence (AI) to automate the development of such tests. By leveraging text-to-speech (TTS) and automatic speech recognition (ASR) technologies, this approach significantly reduces the cost, time, and resources required for high-quality speech-in-noise testing accessible worldwide. 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 ASR-based level corrections to perceptually equalize the digits, demonstrating characteristics comparable to traditional tests. Notably, Aladdin provides a universal guideline for developing DIN tests across languages, addressing the challenge of comparing test results across variants. This approach, with its potential for broad application in audiology, represents a significant advancement in test development and offers a cost-effective and efficient enhancement to global screening and treatment for hearing loss.
Polspoel et al. (Thu,) studied this question.