Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids

TL;DR

This paper introduces a simulation-based, single-microphone method for own voice detection in hearing aids, using transfer function simulations and a transformer classifier to achieve high accuracy and generalize to real-world data.

Contribution

It proposes a novel data augmentation strategy with simulated transfer functions and a hierarchical training approach for ML-based own voice detection in hearing aids.

Findings

95.52% accuracy on simulated head-and-torso data

90.02% accuracy with short utterances

80% accuracy on real recordings without fine-tuning

Abstract

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.