Difficulty understanding speech in noise (SIN) is a major determinant of communication ability and quality of life, affecting individuals with hearing loss as well as some with normal hearing. Data-driven prediction methods offer a promising approach for modeling complex, multidimensional factors underlying speech perception....
✦ The floor
Discussion
Signed responses from readers of the wire.
Machine learning models show promise for predicting speech-in-noise performance, but until clinical validation benchmarks and standardised input features are established, audiologists should not replace behavioural speech-in-noise testing with AI-predicted scores.
A validated machine learning predictor of speech-in-noise performance could transform hearing aid fitting, hearing screening, and audiological assessment by enabling faster, more objective, and potentially remote evaluation of a patient's real-world hearing ability.
- 01Systematic review and meta-analysis in Annals of Biomedical Engineering on ML models predicting speech-in-noise (SIN) performance.
- 02Speech-in-noise performance is a critical real-world hearing metric not always captured by standard pure-tone audiometry.
- 03Multiple ML model types (e.g., regression, neural networks) likely examined; meta-analysis quantifies overall predictive accuracy.
- 04Findings have direct implications for hearing aid fitting algorithms, remote audiological assessment, and screening tools.
- 05Standardisation of input features and outcome measures across studies is a key challenge for clinical translation.
Machine learning models can predict speech-in-noise performance, a key measure of hearing ability and communication quality.
studypartially supported- PMID
- 42859950
- DOI
- 10.1007/s10439-026-04407-z.
- Journal
- Annals of Biomedical Engineering
- Publication type
- systematic_review_and_meta_analysis
- Evidence level
- 1a
- Population
- Individuals whose speech-in-noise performance was modelled across included studies (population varies by constituent studies)
- Intervention
- Machine learning models for predicting speech-in-noise performance
Primary outcomes
Predictive accuracy of machine learning models for speech-in-noise performance; Comparative performance across ML model types