  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.
