Restoring speech intelligibility for hearing aid users with deep learning.

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 diffe

SUBMITTER: Diehl PU 

PROVIDER: S-EPMC9932078 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature