# Speech Synthesis as Augmentation for Low-Resource ASR

arXiv: https://arxiv.org/abs/2012.13004 · HTML: https://mlsanity.com/paper/2012.13004/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Adversarial synthesis based data-augmentation for code-switched spoken language identification](https://mlsanity.com/paper/2205.15747/) (2022)

## Abstract (arXiv)

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.