# Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach

arXiv: https://arxiv.org/abs/2406.01446 · HTML: https://mlsanity.com/paper/2406.01446/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 3 Jun 2024.

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

- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation](https://mlsanity.com/paper/2401.00662/) (2024)
- [XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese](https://mlsanity.com/paper/2401.06832/) (2024)

## Built on by (most cited first, 7 of 7)

- [EuroSpeech: A Multilingual Speech Corpus](https://mlsanity.com/paper/2510.00514/) (2025)
- [Swivuriso: The South African Next Voices Multilingual Speech Dataset](https://mlsanity.com/paper/2512.02201/) (2025)
- [TG-ASR: Translation-Guided Learning with Parallel Gated Cross Attention for Low-Resource Automatic Speech Recognition](https://mlsanity.com/paper/2602.22039/) (2026)
- [Automatic Speech Recognition for the Ika Language](https://mlsanity.com/paper/2410.00940/) (2024)
- [Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages](https://mlsanity.com/paper/2501.14788/) (2025)
- [Assessing the Feasibility of Lightweight Whisper Models for Low-Resource Urdu Transcription](https://mlsanity.com/paper/2508.09865/) (2025)
- [Anchoring Speech with Semantics: A Multimodal Adapter Mechanism for Automatic Speech Recognition in Low-Resource Languages](https://mlsanity.com/paper/2608.29239/) (2026)

## Abstract (arXiv)

In recent years, automatic speech recognition (ASR) systems have significantly improved, especially in languages with a vast amount of transcribed speech data. However, ASR systems tend to perform poorly for low-resource languages with fewer resources, such as minority and regional languages. This study introduces a novel pipeline designed to generate ASR training datasets from audiobooks, which typically feature a single transcript associated with hours-long audios. The common structure of these audiobooks poses a unique challenge due to the extensive length of audio segments, whereas optimal ASR training requires segments ranging from 4 to 15 seconds. To address this, we propose a method for effectively aligning audio with its corresponding text and segmenting it into lengths suitable for ASR training. Our approach simplifies data preparation for ASR systems in low-resource languages and demonstrates its application through a case study involving the Armenian language. Our method, which is "portable" to many low-resource languages, not only mitigates the issue of data scarcity but also enhances the performance of ASR models for underrepresented languages.