Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
Abstract
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech. Previous speech representation methods learn through conditioning on past frames and predicting information about future frames. Whereas Mockingjay is designed to predict the current frame through jointly conditioning on both past and future contexts. The Mockingjay representation improves performance for a wide range of downstream tasks, including phoneme classification, speaker recognition, and sentiment classification on spoken content, while outperforming other approaches. Mockingjay is empirically powerful and can be fine-tuned with downstream models, with only 2 epochs we further improve performance dramatically. In a low resource setting with only 0.1% of labeled data, we outperform the result of Mel-features that uses all 100% labeled data.
Cite
@article{arxiv.1910.12638,
title = {Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders},
author = {Andy T. Liu and Shu-wen Yang and Po-Han Chi and Po-chun Hsu and Hung-yi Lee},
journal= {arXiv preprint arXiv:1910.12638},
year = {2020}
}
Comments
Accepted by ICASSP 2020, Lecture Session