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.

翻译：许多耳内式听力设备包含一个耳内麦克风，可用于在嘈杂环境中捕捉用户的自声。由于耳道闭塞效应，耳内麦克风主要记录体传导语音，因此受到频带限制影响，同时仅能捕捉有限的外部噪声。为了利用旨在联合带宽扩展、均衡和降噪的算法提升耳内麦克风信号质量，需要建立耳道入口与耳内麦克风之间自声传输特性的精确模型。该模型可用于例如生成大量耳内录音以训练基于监督学习的算法。由于先前关于耳道闭塞的研究表明自声传输特性取决于语音内容，本文提出一种基于音素识别的语音依赖系统识别模型。我们通过语音依赖和语音独立建模评估自声语音模拟的准确性，并探究建模方法对不同说话者的泛化能力。仿真结果表明，与使用语音独立模型相比，采用本文提出的语音依赖模型更适用于模拟耳内录音。