Variation Outweighs Syntax: An Empirical Analysis of Data Augmentation for Low-Resource ASR

Katsumi Ibaraki, David Chiang

Keywords: Automatic Speech Recognition, data augmentation, low-resource languages

TL;DR: Standard data augmentation methods have limited benefit for low-resource languages, but combining with simple generation-based augmentation has significant improvements.

Abstract: Standard data augmentation methods like SpecAugment have limited benefit in extremely low-resource settings. Furthermore, low-resource languages suffer from a lack of large text corpora or unlabeled/labeled audio to draw from. To address this limitation, we investigate two simple generation-based augmentation methods: Tag–Based and Random Replacement. Tag-Based Replacement uses only annotations commonly produced as part of the language documentation process, whereas Random Replacement does not use any linguistic information at all. Experiments on four extremely low-resource languages reveal a synergistic effect: while individual modification-based augmentations like SpecAugment yield marginal or inconsistent gains, combining them with generation-based augmentation reduces WER by an average of 4.7\% absolute (10.0\% relative), peaking at 8.0\% (13.5\% relative) reduction for Nashta. Crucially, maximizing variation proves more effective than preserving syntactic coherence.

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Submission Number: 12