  Own voice pickup for hearables in noisy environments benefits from using both
an outer and an in-ear microphone outside and inside the occluded ear. Due to
environmental noise recorded at both microphones, and amplification of the own
voice at low frequencies and band-limitation at the in-ear microphone, an own
voice reconstruction system is needed to enable communication. A large amount
of own voice signals is required to train a supervised deep learning-based own
voice reconstruction system. Training data can either be obtained by recording
a large amount of own voice signals of different talkers with a specific
device, which is costly, or through augmentation of available speech data. Own
voice signals can be simulated by assuming a linear time-invariant relative
transfer function between hearable microphones for each phoneme, referred to as
own voice transfer characteristics. In this paper, we propose data augmentation
techniques for training an own voice reconstruction system based on
speech-dependent models of own voice transfer characteristics between hearable
microphones. The proposed techniques use few recorded own voice signals to
estimate transfer characteristics and can then be used to simulate a large
amount of own voice signals based on single-channel speech signals.
Experimental results show that the proposed speech-dependent individual data
augmentation technique leads to better performance compared to other data
augmentation techniques or compared to training only on the available recorded
own voice signals, and additional fine-tuning on the available recorded signals
can improve performance further.
