Low-Complexity Own Voice Reconstruction for Hearables with an In-Ear Microphone

Hearable devices, equipped with one or more microphones, are commonly used for speech communication. Here, we consider the scenario where a hearable is used to capture the user’s own voice in a noisy environment. In this scenario, own voice reconstruction (OVR) is essential for enhancing the quality and intelligibility of the recorded noisy own voice signals for telephony applications. In previous work, we developed a deep learning-based OVR system, aiming to reduce the amount of device-specific recorded signals for training by using data augmentation with phoneme-dependent models of own voice transfer characteristics. Given the limited computational resources available on hearables, in this paper we propose low-complexity variants of an OVR system based on the frequency and time joint non-linear filter (FT-JNF) architecture and investigate the required amount of device-specific recorded signals for effective data augmentation and fine-tuning. Simulation results show that the proposed OVR system considerably improves speech quality, even under constraints of low complexity and a limited amount of device-specific recorded signals.