ISBN 9783843957656

978-3-8439-5765-6, Reihe Elektrotechnik

Mattes Ohlenbusch
DNN-Based Own Voice Reconstruction for Hearables with an In-Ear Microphone

186 Seiten, Dissertation Carl von Ossietzky Universität Oldenburg (2026), Hardcover, B5

Zusammenfassung / Abstract

In recent years, hearable technology has advanced rapidly, leading to widespread daily use in challenging acoustic environments. As their popularity has grown, so has the demand for high-quality speech communication. Although hearables can capture the user’s own voice with outer microphones, recordings made in noisy conditions typically require processing to enhance speech quality, which can be challenging at high noise levels. Many modern hearables also include an in-ear microphone, which is more robust to environmental noise than the outer microphones because the device partially occludes the ear canal. However, in-ear own voice recordings exhibit characteristic distortions, such as low-frequency amplification and band-limitation, which vary strongly across individuals, change during speech production, and depend on device properties. These effects need to be taken into account when using an in-ear microphone for own voice capture.

The main objective of this thesis is to develop and evaluate causal deep neural network (DNN)-based own voice reconstruction (OVR) approaches that estimate clean broadband speech from noisy outer and in-ear microphone signals. Achieving this objective requires addressing several key challenges: understanding the unique distortions affecting in-ear own voice recordings, reducing the training data requirements of DNN-based OVR systems, meeting realistic computational complexity constraints, identifying suitable objective metrics for OVR performance that correlate well with subjective quality ratings, and investigating the benefits of personalizing OVR systems to individual talkers.