  Automatic speech recognition (ASR) models are typically designed to operate
on a single input data type, e.g. a single or multi-channel audio streamed from
a device. This design decision assumes the primary input data source does not
change and if an additional (auxiliary) data source is occasionally available,
it cannot be used. An ASR model that operates on both primary and auxiliary
data can achieve better accuracy compared to a primary-only solution; and a
model that can serve both primary-only (PO) and primary-plus-auxiliary (PPA)
modes is highly desirable. In this work, we propose a unified ASR model that
can serve both modes. We demonstrate its efficacy in a realistic scenario where
a set of devices typically stream a single primary audio channel, and two
additional auxiliary channels only when upload bandwidth allows it. The
architecture enables a unique methodology that uses both types of input audio
during training time. Our proposed approach achieves up to 12.5% relative
word-error-rate reduction (WERR) compared to a PO baseline, and up to 16.0%
relative WERR in low-SNR conditions. The unique training methodology achieves
up to 2.5% relative WERR compared to a PPA baseline.
