ArticleTrends in hearing
Digits-In-Noise Hearing Test Using Text-to-Speech and Automatic Speech Recognition: Proof-of-Concept Study.
Article in Trends in hearing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Integrated digit-in-noise testing for hearing and cognitive screening.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Automatic Speech Recognition in Healthcare in the Post-LLM Era: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Emerging Speech-in-Noise Tools for the Assessment of Hearing Loss: A Scoping Review.Audiology research · 2026Review
- Digits-In-Noise Hearing Test Using Text-to-Speech and Automatic Speech Recognition: Proof-of-Concept Study.Trends in hearingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This proof-of-concept study evaluated the implementation of a digits-in-noise test we call the 'AI-powered test' that used text-to-speech (TTS) and automatic speech recognition (ASR). Two other digits-in-noise tests formed the baselines for comparison: the 'keyboard-based test' which used the same configurations as the AI-powered test, and the 'independent test', a third-party-sourced test not modified by us. The validity of the AI-powered test was evaluated by measuring its difference from the independent test and comparing it with the baseline, which was the difference between the Keyboard-based test and the Independent test. The reliability of the AI-powered test was measured by comparing the similarity of two runs of this test and the Independent test. The study involved 31 participants: 10 with hearing loss and 21 with normal-hearing. Achieved mean bias and limits-of-agreement showed that the agreement between the AI-powered test and the independent test (-1.3 ± 4.9 dB) was similar to the agreement between the keyboard-based test and the Independent test (-0.2 ± 4.4 dB), indicating that the addition of TTS and ASR did not have a negative impact. The AI-powered test had a reliability of -1.0 ± 5.7 dB, which was poorer than the baseline reliability (-0.4 ± 3.8 dB), but this was improved to -0.9 ± 3.8 dB when outliers were removed, showing that low-error ASR (as shown with the Whisper model) makes the test as reliable as independent tests. These findings suggest that a digits-in-noise test using synthetic stimuli and automatic speech recognition is a viable alternative to traditional tests and could have real-world applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.