A systematic review of machine learning techniques for real-time speech and noise classification in hearing aids

Hearing loss significantly impairs speech comprehension in noisy environments, creating major communication challenges for individuals with hearing impairment. Modern hearing aids increasingly rely on intelligent systems capable of real-time speech and noise classification to enhance speech intelligibility while suppressing background noise....

No immediate practice change warranted, but audiologists should monitor this fast-moving area as ML-based classifiers are approaching real-time clinical deployment and may soon influence device selection counseling.

A systematic review synthesizing machine learning approaches for hearing-aid scene classification provides a critical evidence base for the next generation of AI-driven hearing aid signal processing.

1. 01Systematic review published in Disability and Rehabilitation: Assistive Technology (DOI: 10.1080/17483107.2026.2671834).
2. 02Reviews machine learning (ML) techniques for real-time speech vs. noise classification in hearing aids.
3. 03Goal is to improve speech intelligibility (clarity) in noisy environments for hearing aid users.
4. 04Synthesizes the state of the field to inform future hearing aid signal processing design.
5. 05As a systematic review, represents the highest non-RCT level of evidence synthesis for this technology domain.

PMID

42126409

DOI

10.1080/17483107.2026.2671834.

Journal

Disability and Rehabilitation: Assistive Technology

Publication type

systematic_review

Evidence level

2a

Population

Studies involving machine learning algorithms applied to hearing aid speech and noise classification tasks

Intervention

Machine learning techniques for real-time speech and noise classification in hearing aids

Comparator

Conventional hearing aid signal processing approaches (as reported in included studies)