  Manual correction of speech transcription can involve a selection from
plausible transcriptions. Recent work has shown the feasibility of employing a
mismatched crowd for speech transcription. However, it is yet to be established
whether a mismatched worker has sufficiently fine-granular speech perception to
choose among the phonetically proximate options that are likely to be generated
from the trellis of an ASRU. Hence, we consider five languages, Arabic, German,
Hindi, Russian and Spanish. For each we generate synthetic, phonetically
proximate, options which emulate post-editing scenarios of varying difficulty.
We consistently observe non-trivial crowd ability to choose among fine-granular
options.
