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arXiv · 1803.08991

Leveraging translations for speech transcription in low-resource settings

Antonis Anastasopoulos·David Chiang
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Abstract

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%.

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Antonis Anastasopoulos, David Chiang. 2018-03-23. Leveraging translations for speech transcription in low-resource settings. https://arxiv.org/abs/1803.08991

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