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

TL;DR

This paper introduces a speech-dependent model for simulating in-ear microphone signals in hearables, improving the accuracy of voice transfer characteristic modeling by incorporating phoneme recognition, which enhances signal processing in noisy environments.

Contribution

It proposes a novel speech-dependent system identification approach based on phoneme recognition for modeling in-ear microphone transfer characteristics, outperforming speech-independent models.

Findings

Speech-dependent model yields more accurate in-ear voice simulations.

Model generalizes well across different talkers.

Speech-dependent approach outperforms speech-independent methods.

Abstract

Many hearables contain an in-ear microphone, which may be used to capture the own voice of its user in noisy environments. Since the in-ear microphone mostly records body-conducted speech due to ear canal occlusion, it suffers from band-limitation effects while only capturing a limited amount of external noise. To enhance the quality of the in-ear microphone signal using algorithms aiming at joint bandwidth extension, equalization, and noise reduction, it is desirable to have an accurate model of the own voice transfer characteristics between the entrance of the ear canal and the in-ear microphone. Such a model can be used, e.g., to simulate a large amount of in-ear recordings to train supervised learning-based algorithms. Since previous research on ear canal occlusion suggests that own voice transfer characteristics depend on speech content, in this contribution we propose a speech-dependent system identification model based on phoneme recognition. We assess the accuracy of simulating own voice speech by speech-dependent and speech-independent modeling and investigate how well modeling approaches are able to generalize to different talkers. Simulation results show that using the proposed speech-dependent model is preferable for simulating in-ear recordings compared to using a speech-independent model.