  For many low-resource or endangered languages, spoken language resources are
more likely to be annotated with translations than with transcriptions. Recent
work exploits such annotations to produce speech-to-translation alignments,
without access to any text transcriptions. We investigate whether providing
such information can aid in producing better (mismatched) crowdsourced
transcriptions, which in turn could be valuable for training speech recognition
systems, and show that they can indeed be beneficial through a small-scale case
study as a proof-of-concept. We also present a simple phonetically aware string
averaging technique that produces transcriptions of higher quality.
