Noise-Robust Hearing Aid Voice Control

TL;DR

This paper introduces a new noisy speech dataset from hearing aids, evaluates keyword spotting models on it, and shows that combining bone-conducted speech with microphone signals improves noise-robust voice control.

Contribution

It provides a novel hearing aid speech dataset and baseline results demonstrating the benefits of combining microphone signals for noise-robust voice control.

Findings

Bone-conducted speech improves noise robustness.

Combining BTE microphone signals with BCS boosts performance.

Public dataset availability facilitates future research.

Abstract

Advancing the design of robust hearing aid (HA) voice control is crucial to increase the HA use rate among hard of hearing people as well as to improve HA users' experience. In this work, we contribute towards this goal by, first, presenting a novel HA speech dataset consisting of noisy own voice captured by 2 behind-the-ear (BTE) and 1 in-ear-canal (IEC) microphones. Second, we provide baseline HA voice control results from the evaluation of light, state-of-the-art keyword spotting models utilizing different combinations of HA microphone signals. Experimental results show the benefits of exploiting bandwidth-limited bone-conducted speech (BCS) from the IEC microphone to achieve noise-robust HA voice control. Furthermore, results also demonstrate that voice control performance can be boosted by assisting BCS by the broader-bandwidth BTE microphone signals. Aiming at setting a baseline upon which the scientific community can continue to progress, the HA noisy speech dataset has been made publicly available.