Modeling of Speech-dependent Own Voice Transfer Characteristics for Hearables with In-ear Microphones

TL;DR

This paper introduces a speech-dependent model for own voice transfer in hearables with in-ear microphones, improving simulation accuracy over speech-independent models and demonstrating better generalization across talkers.

Contribution

It proposes a novel phoneme-based linear time-invariant transfer function model for own voice transfer characteristics in hearables, accounting for speech content and individual differences.

Findings

Speech-dependent models outperform speech-independent models in simulation accuracy.

Talker-averaged models generalize better to new talkers.

Experimental validation with prototype hearables confirms improved modeling performance.

Abstract

Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user. However, due to the hearable occluding the ear canal, the in-ear microphone mostly records body-conducted speech, typically suffering from band-limitation effects and amplification at low frequencies. Since the occlusion effect is determined by the ratio between the air-conducted and body-conducted components of own voice, the own voice transfer characteristics between the outer face of the hearable and the in-ear microphone depend on the speech content and the individual talker. In this paper, we propose a speech-dependent model of the own voice transfer characteristics based on phoneme recognition, assuming a linear time-invariant relative transfer function for each phoneme. We consider both individual models as well as models averaged over several talkers. Experimental results based on recordings with a prototype hearable show that the proposed speech-dependent model enables to simulate in-ear signals more accurately than a speech-independent model in terms of technical measures, especially under utterance mismatch and talker mismatch. Additionally, simulation results show that talker-averaged models generalize better to different talkers than individual models.