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Updated: May 13, 2025

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

Sigrid Polspoel1,2, David R Moore3,4, De Wet Swanepoel5,6

  • 1Otolaryngology-Head and Neck Surgery, Section Ear and Hearing, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan, Amsterdam, The Netherlands.

Scientific Reports
|April 15, 2025
PubMed
Summary
This summary is machine-generated.

Developing artificial intelligence (AI)-based speech-in-noise hearing tests automates creation, reducing costs and improving accessibility. The Aladdin system demonstrates high accuracy, offering a universal solution for global hearing loss screening.

Keywords:
AladdinArtificial intelligence (AI)Automatic speech recognition (ASR)Digits-in-noise testSynthetic speechText-to-speech (TTS)

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Area of Science:

  • Audiology
  • Artificial Intelligence
  • Speech Processing

Background:

  • Hearing loss significantly impacts communication, especially in noisy environments.
  • Traditional speech-in-noise tests are crucial but costly and time-consuming to develop.
  • Accessibility of these tests is limited, particularly in low-resource settings.

Purpose of the Study:

  • To introduce an AI-based method for automating the development of speech-in-noise hearing tests.
  • To reduce the cost, time, and resources needed for creating high-quality hearing tests.
  • To establish a universal guideline for developing language-independent hearing tests.

Main Methods:

  • Utilized text-to-speech and automatic speech recognition (ASR) technologies.
  • Developed the "Aladdin" (Automatic LAnguage-independent Development of the digits-in-noise test) procedure.
  • Created synthetic speech material for digits-in-noise (DIN) tests and employed ASR for level corrections.

Main Results:

  • Aladdin tests demonstrated high diagnostic accuracy: 84% specificity and 100% sensitivity.
  • Performance was comparable to traditional reference DIN tests (87% specificity, 100% sensitivity).
  • Validated the approach with Dutch and English language tests in participants with normal and impaired hearing.

Conclusions:

  • The Aladdin approach offers an efficient and cost-effective method for developing universal DIN hearing tests.
  • This AI-driven innovation significantly enhances global hearing loss screening and treatment accessibility.
  • Provides a standardized framework for cross-linguistic comparison of hearing test results.