Yao-Fei Cheng

Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages

2024 · arXiv (Cornell University)

This study investigates the efficacy of data augmentation techniques for low-resource automatic speech recognition (ASR), focusing on two endangered Austronesian languages, Amis and Seediq. Recognizing the potential of self-supervised learning (SSL) in low-resource settings, we …