# Restoring speech intelligibility for hearing aid users with deep learning

arXiv: https://arxiv.org/abs/2206.11567 · HTML: https://mlsanity.com/paper/2206.11567/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2022.

## Built on by (most cited first, 3 of 3)

- [Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization](https://mlsanity.com/paper/2210.09134/) (2022)
- [Evaluation of Deep Audio Representations for Hearables](https://mlsanity.com/paper/2502.06664/) (2025)
- [Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellation](https://mlsanity.com/paper/2507.07043/) (2025)

## Abstract (arXiv)

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.