# Data Augmentation and Hyperparameter Tuning for Low-Resource MFA

arXiv: https://arxiv.org/abs/2504.07024 · HTML: https://mlsanity.com/paper/2504.07024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition](https://mlsanity.com/paper/2102.12664/) (2021)
- [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://mlsanity.com/paper/2111.09618/) (2021)
- [Data Augmentation for Neural Machine Translation using Generative Language Model](https://mlsanity.com/paper/2307.16833/) (2023)
- [Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages](https://mlsanity.com/paper/2103.14583/) (2021)
- [An Ensemble 1D-CNN-LSTM-GRU Model with Data Augmentation for Speech Emotion Recognition](https://mlsanity.com/paper/2112.05666/) (2021)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)

## Built on by (most cited first, 1 of 1)

- [Explainable AI: XAI-Guided Context-Aware Data Augmentation](https://mlsanity.com/paper/2506.03484/) (2025)

## Abstract (arXiv)

A continued issue for those working with computational tools and endangered and under-resourced languages is the lower accuracy of results for languages with smaller amounts of data. We attempt to ameliorate this issue by using data augmentation methods to increase corpus size, comparing augmentation to hyperparameter tuning for multilingual forced alignment. Unlike text augmentation methods, audio augmentation does not lead to substantially increased performance. Hyperparameter tuning, on the other hand, results in substantial improvement without (for this amount of data) infeasible additional training time. For languages with small to medium amounts of training data, this is a workable alternative to adapting models from high-resource languages.