Speech recognition of under-resourced languages using mismatched transcriptions

Mismatched crowdsourcing is a technique to derive speech transcriptions using crowd-workers unfamiliar with the language being spoken. This technique is especially useful for under-resourced languages since it is hard to hire native transcribers. In this paper, we demonstrate that using mismatched transcription for adaptation improves performance of speech recognition under limited matched training data conditions. In addition, we show that using data augmentation improves not only performance of monolingual system but also makes mismatched transcription adaptation more effective.