  Wearable devices like smart glasses are approaching the compute capability to
seamlessly generate real-time closed captions for live conversations. We build
on our recently introduced directional Automatic Speech Recognition (ASR) for
smart glasses that have microphone arrays, which fuses multi-channel ASR with
serialized output training, for wearer/conversation-partner disambiguation as
well as suppression of cross-talk speech from non-target directions and noise.
  When ASR work is part of a broader system-development process, one may be
faced with changes to microphone geometries as system development progresses.
  This paper aims to make multi-channel ASR insensitive to limited variations
of microphone-array geometry. We show that a model trained on multiple similar
geometries is largely agnostic and generalizes well to new geometries, as long
as they are not too different. Furthermore, training the model this way
improves accuracy for seen geometries by 15 to 28\% relative. Lastly, we refine
the beamforming by a novel Non-Linearly Constrained Minimum Variance criterion.
