# Building Community-Centred NLP Resources for Puno Quechua

arXiv: https://arxiv.org/abs/2605.28253 · HTML: https://mlsanity.com/paper/2605.28253/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 4 of 4)

- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Improving Low-Resource Speech Recognition with Pretrained Speech Models: Continued Pretraining vs. Semi-Supervised Training](https://mlsanity.com/paper/2207.00659/) (2022)
- [Continued Pretraining for Low-Resource Swahili ASR: Achieving State-of-the-Art Performance with Minimal Labeled Data](https://mlsanity.com/paper/2603.11378/) (2026)
- [Quechua Speech Datasets in Common Voice: The Case of Puno Quechua](https://mlsanity.com/paper/2510.13871/) (2025)

## Abstract (arXiv)

The preservation of under-resourced languages requires digital tools and resources shaped by and for their speakers. We present the first dedicated ASR resources for Puno Quechua (ISO 639-3: qxp): (1) the largest speech corpus for any single Quechua variety, consisting in 66 hours of recordings for scripted and spontaneous speech (including 36 hours of manually transcribed and validated data), collected via a participatory design campaign; (2) the first systematic ASR benchmark for Puno Quechua, evaluating state-of-the-art models and fine-tuning Whisper-base, wav2vec2-base, and XLS-R-300M, with and without continued pre-training (CPT); (3) an open release of all datasets and fine-tuned models.