Training Strategies for Own Voice Reconstruction in Hearing Protection Devices using an In-ear Microphone
arXiv:2205.06157 · doi:10.1109/IWAENC53105.2022.9914801
Abstract
In-ear microphones in hearing protection devices can be utilized to capture the own voice speech of the person wearing the devices in noisy environments. Since in-ear recordings of the own voice are typically band-limited, an own voice reconstruction system is required to recover clean broadband speech from the in-ear signals. However, the availability of speech data for this scenario is typically limited due to device-specific transfer characteristics and the need to collect data from in-situ measurements. In this paper, we apply a deep learning-based bandwidth-extension system to the own voice reconstruction task and investigate different training strategies in order to overcome the limited availability of training data. Experimental results indicate that the use of simulated training data based on recordings of several talkers in combination with a fine-tuning approach using real data is advantageous compared to directly training on a small real dataset.
Accepted to IWAENC 2022
Cited by in corpus (6)
- Configurable EBEN: Extreme Bandwidth Extension Network to enhance body-conducted speech capture
- Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones
- Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments
- Speech-dependent Modeling of Own Voice Transfer Characteristics for In-ear Microphones in Hearables
- Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone
- Speech-dependent Data Augmentation for Own Voice Reconstruction with Hearable Microphones in Noisy Environments