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Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments

arXiv 2104.00824 ↗First posted 2 Apr 2021cs.CL cs.SD eess.AScited by 5

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Abstract (arXiv)

There is growing interest in ASR systems that can recognize phones in a language-independent fashion. There is additionally interest in building language technologies for low-resource and endangered languages. However, there is a paucity of realistic data that can be used to test such systems and technologies. This paper presents a publicly available, phonetically transcribed corpus of 2255 utterances (words and short phrases) in the endangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman language variety spoken mostly in India. Because the dataset is transcribed in terms of phones, rather than phonemes, it is a better match for universal phone recognition systems than many larger (phonemically transcribed) datasets. This paper describes the dataset and the methodology used to produce it. It further presents basic benchmarks of state-of-the-art universal phone recognition systems on the dataset as baselines for future experiments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 2 Apr 2021.

Builds on

3 of 14 references.

References · 3

  1. 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
  2. 2020Towards Zero-shot Learning for Automatic Phonemic Transcriptioncited by 14
  3. 2020AlloVera: A Multilingual Allophone Databasecited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1; 2026: 3.

5 papers.