ArticleScientific reports2023
Restoring speech intelligibility for hearing aid users with deep learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Speech Perception in Noise: A Narrative Review of Cognitive Contribution and Hearing-Aid Signal Processing.Journal of Intelligence · 2026Review
- An Intelligent Directionality System for Hearing Aids Incorporating Deep Neural Networks: Improving Speech Understanding While Preserving Spatial Awareness.Audiology research · 2026Article
- Cancer therapy-induced ototoxicity: Current challenges and emerging management strategies.Animal models and experimental medicine · 2026Review
- Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026Review
- Benchmarking speech-to-text robustness in noisy emergency medical dialogues: an evaluation of models under realistic acoustic conditions.JAMIA open · 2025Article
- Brain benefits of deep learning-based noise management in experienced hearing aid users using functional near infrared spectroscopy.Scientific reports · 2025Article
- AI-Based Prediction of Bone Conduction Thresholds Using Air Conduction Audiometry Data.Journal of clinical medicine · 2025Article
- A brain-inspired algorithm improves "cocktail party" listening for individuals with hearing loss.Communications engineering · 2025Article
- Speech and music source separation for cochlear implant users: front-end and end-to-end approach.Frontiers in neuroscience · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- Toward Alleviating the Stigma of Hearing Aids: A Review.Audiology research · 2024Review
- An ideal compressed mask for increasing speech intelligibility without sacrificing environmental sound recognitiona).The Journal of the Acoustical Society of America · 2024Article
- Mixed T-domain and TF-domain Magnitude and Phase representations for GAN-based speech enhancement.Scientific reports · 2024Article
- Progress made in the efficacy and viability of deep-learning-based noise reduction.The Journal of the Acoustical Society of America · 2023Article
- Non-intrusive deep learning-based computational speech metrics with high-accuracy across a wide range of acoustic scenes.PloS one · 2022Article
- Objective Evaluation of a Deep Learning-Based Noise Reduction Algorithm for Hearing Aids Under Diverse Fitting and Listening Conditions.Trends in hearingArticle
- Toward an Extended Classification of Noise-Distortion Preferences by Modeling Longitudinal Dynamics of Listening Choices.Trends in hearingArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Almost half a billion people world-wide suffer from disabling hearing loss. While hearing aids can partially compensate for this, a large proportion of users struggle to understand speech in situations with background noise. Here, we present a deep learning-based algorithm that selectively suppresses noise while maintaining speech signals. The algorithm restores speech intelligibility for hearing aid users to the level of control subjects with normal hearing. It consists of a deep network that is trained on a large custom database of noisy speech signals and is further optimized by a neural architecture search, using a novel deep learning-based metric for speech intelligibility. The network achieves state-of-the-art denoising on a range of human-graded assessments, generalizes across different noise categories and-in contrast to classic beamforming approaches-operates on a single microphone. The system runs in real time on a laptop, suggesting that large-scale deployment on hearing aid chips could be achieved within a few years. Deep learning-based denoising therefore holds the potential to improve the quality of life of millions of hearing impaired people soon.
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.