Although humans excel at speech recognition, recognition accuracy can vary widely due to differences in background environments as well as the speaker's voice quality, intonation, and pitch. Predicting when speech recognition will succeed or fail, however, remains an ongoing challenge in hearing research....
This preprint offers a potentially valuable acoustic framework for predicting speech-in-noise performance, but findings require peer review and clinical validation before influencing hearing aid fitting or speech-in-noise test selection.
A robust acoustic predictor of speech recognition in noise could transform how clinicians and engineers evaluate and design hearing devices and diagnostic speech tests.
- 01Modulation statistics (patterns of how sound fluctuates over time) predict speech recognition accuracy.
- 02Predictions hold across varied words, talker voices, and real-world background sounds.
- 03Study is a bioRxiv preprint — not yet peer-reviewed.
- 04Could inform hearing aid signal processing algorithms and clinical speech-in-noise testing.
- 05Approach may generalize beyond lab stimuli to ecologically valid listening environments.
Modulation statistics of audio signals robustly predict speech recognition accuracy across many words, voices, and natural background sounds.
studypartially supportedThe predictive model generalizes across diverse listening conditions including natural background sounds.
studypartially supported- PMID
- 42094472
- DOI
- 10.64898/2026.04.27.721224.
- Publication type
- research_article
- Evidence level
- 2b
- Population
- Speech recognition test stimuli across multiple words, voices, and background sound environments
- Intervention
- Modulation statistics of audio signals as a predictor of speech recognition
- Comparator
- Alternative acoustic predictors / baseline speech recognition models
Primary outcomes
Speech recognition accuracy across varied words and voices; Prediction robustness across natural background sound conditions