Leveraging natural language processing models to automate speech-intelligibility scoring
Bibliographic record
Abstract
Assessment of speech intelligibility in noise is critical for measuring the impact of age-related hearing loss. However, quantifying intelligibility often requires a human to manually process responses provided by a participant or patient to obtain a speech-intelligibility score – typically the proportion of correctly heard words. This manual process can be time-consuming and thus costly. The current study investigates whether state-of-the-art Natural Language Processing (NLP) models from Google and OpenAI could be used to calculate speech-intelligibility scores as an alternative to human scoring. It was specifically tested whether NLP models capture common speech-in-noise perception phenomena in younger and older adults (N = 144) listening to speech masked by modulated or unmodulated babble noise. The results show that NLP speech-intelligibility scores closely matched intelligibility scores from a human scorer (r ∼0.95). The main difference is, on average, ∼2% underestimation of NLP intelligibility scores relative to human intelligibility scores for moderate to high signal-to-noise ratios. This underestimation results from participants making minor errors related to misspellings, gender, or tense, to which NLP models are sensitive, but human scorers typically correct prior to scoring. Critically, NLP models capture the known age-related reduction in intelligibility and the age-related reduction in the benefit from a modulated relative to an unmodulated masker. OpenAI’s ADA2 appears to perform the best out of the tested NLP models, showing no difference in the speech-in-noise phenomena compared to human scoring. The current study suggests that modern NLP models can be used to score speech-intelligibility data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".