Speech synthesis might hold the key to low-resource speech recognition. Data augmentation techniques have become an essential part of modern speech recognition training. Yet, they are simple, naive, and rarely reflect real-world conditions. Meanwhile, speech synthesis techniques have been rapidly getting closer to the goal of achieving human-like speech. In this paper, we investigate the possibility of using synthesized speech as a form of data augmentation to lower the resources necessary to build a speech recognizer. We experiment with three different kinds of synthesizers: statistical parametric, neural, and adversarial. Our findings are interesting and point to new research directions for the future.
@article{arxiv.2012.13004,
title = {Speech Synthesis as Augmentation for Low-Resource ASR},
author = {Deblin Bagchi and Shannon Wotherspoon and Zhuolin Jiang and Prasanna Muthukumar},
journal= {arXiv preprint arXiv:2012.13004},
year = {2020}
}