  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.
