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Digits-In-Noise Hearing Test Using Text-to-Speech and Automatic Speech Recognition: Proof-of-Concept Study
Mohsen Fatehifar1, Kevin J Munro1,2, Michael A Stone1
1Manchester Centre for Audiology and Deafness (ManCAD), University of Manchester, Manchester, UK.
Trends in Hearing
|October 1, 2025
Summary
An AI-powered digits-in-noise test using text-to-speech and automatic speech recognition shows promise. This new method is a viable alternative to traditional tests, offering similar validity and improved reliability with advanced speech recognition models.
Area of Science:
- Audiology
- Speech Technology
- Artificial Intelligence
Background:
- Traditional digits-in-noise tests are crucial for assessing speech recognition in noisy environments.
- Evaluating new technologies like text-to-speech (TTS) and automatic speech recognition (ASR) for auditory assessments is essential.
Purpose of the Study:
- To evaluate the implementation and performance of an AI-powered digits-in-noise test.
- To compare the validity and reliability of this AI-powered test against traditional keyboard-based and independent tests.
Main Methods:
- Developed an AI-powered digits-in-noise test utilizing TTS and ASR.
- Recruited 31 participants (10 with hearing loss, 21 normal-hearing).
- Assessed validity by comparing AI-test results with an independent test and baseline, and reliability by comparing test-retest similarity.
Main Results:
- The AI-powered test demonstrated comparable validity to the keyboard-based test, with similar agreement levels (-1.3 ± 4.9 dB vs. -0.2 ± 4.4 dB).
- Initial reliability was poorer (-1.0 ± 5.7 dB) but improved significantly (-0.9 ± 3.8 dB) after outlier removal, especially with low-error ASR (Whisper model).
- Agreement between the AI-powered test and the independent test was -1.3 ± 4.9 dB.
Conclusions:
- An AI-powered digits-in-noise test using synthetic stimuli and ASR is a viable alternative to traditional methods.
- The integration of TTS and ASR does not negatively impact test validity.
- With optimized ASR, the AI-powered test achieves reliability comparable to independent tests, suggesting real-world applicability.
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