# Optimising Hearing Aid Fittings for Speech in Noise with a Differentiable Hearing Loss Model

arXiv: https://arxiv.org/abs/2106.04639 · HTML: https://mlsanity.com/paper/2106.04639/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 8 Jun 2021.

## Built on by (most cited first, 2 of 2)

- [Auditory-Based Data Augmentation for End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2204.04284/) (2022)
- [Using Speech Foundational Models in Loss Functions for Hearing Aid Speech Enhancement](https://mlsanity.com/paper/2407.13333/) (2024)

## Abstract (arXiv)

Current hearing aids normally provide amplification based on a general prescriptive fitting, and the benefits provided by the hearing aids vary among different listening environments despite the inclusion of noise suppression feature. Motivated by this fact, this paper proposes a data-driven machine learning technique to develop hearing aid fittings that are customised to speech in different noisy environments. A differentiable hearing loss model is proposed and used to optimise fittings with back-propagation. The customisation is reflected on the data of speech in different noise with also the consideration of noise suppression. The objective evaluation shows the advantages of optimised custom fittings over general prescriptive fittings.