# Cost Analysis of Human-corrected Transcription for Predominately Oral Languages

arXiv: https://arxiv.org/abs/2510.12781 · HTML: https://mlsanity.com/paper/2510.12781/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Speech Recognition with Augmented Synthesized Speech](https://mlsanity.com/paper/1909.11699/) (2019)
- [Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation](https://mlsanity.com/paper/2406.17363/) (2024)
- [The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages](https://mlsanity.com/paper/2505.20564/) (2025)
- [SANTLR: Speech Annotation Toolkit for Low Resource Languages](https://mlsanity.com/paper/1908.01067/) (2019)
- [Synthetic Voice Data for Automatic Speech Recognition in African Languages](https://mlsanity.com/paper/2507.17578/) (2025)

## Built on by (most cited first, 3 of 3)

- [Dealing with the Hard Facts of Low-Resource African NLP](https://mlsanity.com/paper/2511.18557/) (2025)
- [Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara](https://mlsanity.com/paper/2512.19400/) (2025)
- [Listen, Attend, Understand: a Regularization Technique for Stable E2E Speech Translation Training on High Variance labels](https://mlsanity.com/paper/2601.01121/) (2026)

## Abstract (arXiv)

Creating speech datasets for low-resource languages is a critical yet poorly understood challenge, particularly regarding the actual cost in human labor. This paper investigates the time and complexity required to produce high-quality annotated speech data for a subset of low-resource languages, low literacy Predominately Oral Languages, focusing on Bambara, a Manding language of Mali. Through a one-month field study involving ten transcribers with native proficiency, we analyze the correction of ASR-generated transcriptions of 53 hours of Bambara voice data. We report that it takes, on average, 30 hours of human labor to accurately transcribe one hour of speech data under laboratory conditions and 36 hours under field conditions. The study provides a baseline and practical insights for a large class of languages with comparable profiles undertaking the creation of NLP resources.