Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation

Understand

The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech is available.

• Unfortunately, for low-resource languages, such as minority languages, regional languages or dialects, ASR performance generally remains much lower.
• In this study, we investigate whether data augmentation techniques could help improve low-resource ASR performance, focusing on four typologically diverse minority languages or language variants (West Germanic: Gronings, West-Frisian; Malayo-Polynesian: Besemah, Nasal).
• For all four languages, we examine the use of self-training, where an ASR system trained with the available human-transcribed data is used to generate transcriptions, which are then combined with the original data to train a new ASR system.