# Automatic Speech Recognition of Low-Resource Languages Based on Chukchi

arXiv: https://arxiv.org/abs/2210.05726 · HTML: https://mlsanity.com/paper/2210.05726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Unsupervised Speech Recognition](https://mlsanity.com/paper/2105.11084/) (2021)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [Unsupervised speech representation learning using WaveNet autoencoders](https://mlsanity.com/paper/1901.08810/) (2019)
- [Almost Unsupervised Text to Speech and Automatic Speech Recognition](https://mlsanity.com/paper/1905.06791/) (2019)
- [Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition](https://mlsanity.com/paper/2012.11896/) (2020)

## Built on by (most cited first, 2 of 2)

- [ManWav: The First Manchu ASR Model](https://mlsanity.com/paper/2406.13502/) (2024)
- [A Deep Learning Automatic Speech Recognition Model for Shona Language](https://mlsanity.com/paper/2507.21331/) (2025)

## Abstract (arXiv)

The following paper presents a project focused on the research and creation of a new Automatic Speech Recognition (ASR) based in the Chukchi language. There is no one complete corpus of the Chukchi language, so most of the work consisted in collecting audio and texts in the Chukchi language from open sources and processing them. We managed to collect 21:34:23 hours of audio recordings and 112,719 sentences (or 2,068,273 words) of text in the Chukchi language. The XLSR model was trained on the obtained data, which showed good results even with a small amount of data. Besides the fact that the Chukchi language is a low-resource language, it is also polysynthetic, which significantly complicates any automatic processing. Thus, the usual WER metric for evaluating ASR becomes less indicative for a polysynthetic language. However, the CER metric showed good results. The question of metrics for polysynthetic languages remains open.