Feasibility of Post-Editing Speech Transcriptions with a Mismatched Crowd
This addresses the problem of efficient post-editing for speech transcription systems, though it appears incremental as it builds on prior feasibility work.
The study investigated whether a mismatched crowd can accurately select among phonetically similar speech transcription options, finding consistent non-trivial ability across five languages.
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.