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Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages

arXiv 2206.01205 ↗First posted 1 Jun 2022eess.AS cs.LG cs.SDcited by 1

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Abstract (arXiv)

Automatic Speech Recognition (ASR) has increasing utility in the modern world. There are a many ASR models available for languages with large amounts of training data like English. However, low-resource languages are poorly represented. In response we create and release an open-licensed and formatted dataset of audio recordings of the Bible in low-resource northern Indian languages. We setup multiple experimental splits and train and analyze two competitive ASR models to serve as the baseline for future research using this data.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 2 of its 17 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

2 of 17 references.

References · 2

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589

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