Restoring speech intelligibility for hearing aid users with deep learning

TL;DR

This paper introduces a deep learning algorithm that enhances speech intelligibility for hearing aid users by effectively suppressing background noise, operating in real time, and outperforming traditional methods across various noise conditions.

Contribution

It presents a novel deep learning-based denoising system optimized with neural architecture search, achieving state-of-the-art speech enhancement for hearing aids with real-time processing capabilities.

Findings

Restores speech intelligibility to normal hearing levels.

Operates effectively across diverse noise types.

Runs in real time on standard laptops.

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.