  Recently proposed data collection frameworks for endangered language
documentation aim not only to collect speech in the language of interest, but
also to collect translations into a high-resource language that will render the
collected resource interpretable. We focus on this scenario and explore whether
we can improve transcription quality under these extremely low-resource
settings with the assistance of text translations. We present a neural
multi-source model and evaluate several variations of it on three low-resource
datasets. We find that our multi-source model with shared attention outperforms
the baselines, reducing transcription character error rate by up to 12.3%.
