Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids
This research addresses the problem of improving user comfort and speech intelligibility in hearing aids for individuals with hearing impairments, offering a potentially more cost-effective and practical solution.
This study tackled the problem of own voice detection in hearing aids using a single microphone, achieving 95.52% accuracy on simulated head-and-torso test data and 80% accuracy on real hearing aid recordings. The model maintained 90.02% accuracy with one-second utterances under short-duration conditions.
This paper presents a simulation-based approach to own voice detection (OVD) in hearing aids using a single microphone. While OVD can significantly improve user comfort and speech intelligibility, existing solutions often rely on multiple microphones or additional sensors, increasing device complexity and cost. To enable ML-based OVD without requiring costly transfer-function measurements, we propose a data augmentation strategy based on simulated acoustic transfer functions (ATFs) that expose the model to a wide range of spatial propagation conditions. A transformer-based classifier is first trained on analytically generated ATFs and then progressively fine-tuned using numerically simulated ATFs, transitioning from a rigid-sphere model to a detailed head-and-torso representation. This hierarchical adaptation enabled the model to refine its spatial understanding while maintaining generalization. Experimental results show 95.52% accuracy on simulated head-and-torso test data. Under short-duration conditions, the model maintained 90.02% accuracy with one-second utterances. On real hearing aid recordings, the model achieved 80% accuracy without fine-tuning, aided by lightweight test-time feature compensation. This highlights the model's ability to generalize from simulated to real-world conditions, demonstrating practical viability and pointing toward a promising direction for future hearing aid design.