论文标题
使用入耳式麦克风在听力保护设备中进行语音重建的培训策略
Training Strategies for Own Voice Reconstruction in Hearing Protection Devices using an In-ear Microphone
论文作者
论文摘要
听力保护设备中的入耳式麦克风可用于捕捉在嘈杂环境中戴上设备的人自己的语音演讲。由于通常是频段限制自己的声音的入耳式录音，因此需要自己的语音重建系统才能从入耳式信号中恢复清洁的宽带语音。但是，由于设备特定的传输特性以及需要从原位测量值中收集数据，因此对于这种情况的语音数据的可用性通常受到限制。在本文中，我们将基于深度学习的带宽扩展系统应用于自己的语音重建任务，并研究不同的培训策略，以克服有限的培训数据可用性。实验结果表明，与直接在小型真实数据集中的直接培训相比，使用真实数据的微调方法结合几个讲话者的记录以及使用模拟培训数据的使用是有利的。
In-ear microphones in hearing protection devices can be utilized to capture the own voice speech of the person wearing the devices in noisy environments. Since in-ear recordings of the own voice are typically band-limited, an own voice reconstruction system is required to recover clean broadband speech from the in-ear signals. However, the availability of speech data for this scenario is typically limited due to device-specific transfer characteristics and the need to collect data from in-situ measurements. In this paper, we apply a deep learning-based bandwidth-extension system to the own voice reconstruction task and investigate different training strategies in order to overcome the limited availability of training data. Experimental results indicate that the use of simulated training data based on recordings of several talkers in combination with a fine-tuning approach using real data is advantageous compared to directly training on a small real dataset.
