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NoLoR: An ASR-Based Framework for Expedited Endangered Language Documentation with Neo-Aramaic as a Case Study

arXiv 2412.04717 ↗First posted 6 Dec 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

The documentation of the Neo-Aramaic dialects before their extinction has been described as the most urgent task in all of Semitology today. The death of this language will be an unfathomable loss to the descendents of the indigenous speakers of Aramaic, now predominantly diasporic after forced displacement due to violence. This paper develops an ASR model to expedite the documentation of this endangered language and generalizes the strategy in a new framework we call NoLoR.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 6 Dec 2024.

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References · 8

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  3. 2017Exploring Generalization in Deep Learningcited by 595
  4. 2020A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarioscited by 170
  5. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 108
  6. 2021Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognitioncited by 47
  7. 2020LRSpeech: Extremely Low-Resource Speech Synthesis and Recognitioncited by 43
  8. 2021Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox'ochitl Mixteccited by 15

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