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

arXiv: https://arxiv.org/abs/2409.04136 · HTML: https://mlsanity.com/paper/2409.04136/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 6 Sep 2024.

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

- [Real-time speech enhancement in noise for throat microphone using neural audio codec as foundation model](https://mlsanity.com/paper/2508.02974/) (2025)
- [PAS-SE: Personalized Auxiliary-Sensor Speech Enhancement for Voice Pickup in Hearables](https://mlsanity.com/paper/2509.20875/) (2025)

## Abstract (arXiv)

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. In previous work, we developed a deep learning-based OVR system, aiming to reduce the amount of device-specific recordings 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 FT-JNF architecture and investigate the required amount of device-specific recordings 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 recordings.