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Speech Synthesis as Augmentation for Low-Resource ASR

2020-12-23 22:19:42
Deblin Bagchi, Shannon Wotherspoon, Zhuolin Jiang, Prasanna Muthukumar
arXiv_CL
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2012.13004

PDF

https://arxiv.org/pdf/2012.13004.pdf

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