DinoSR: Self-Distillation and Online Clustering for Self-Supervised Speech Representation Learning
Abstract
In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for speech. DinoSR first extracts contextualized embeddings from the input audio with a teacher network, then runs an online clustering system on the embeddings to yield a machine-discovered phone inventory, and finally uses the discretized tokens to guide a student network. We show that DinoSR surpasses previous state-of-the-art performance in several downstream tasks, and provide a detailed analysis of the model and the learned discrete units.
Cite
Text
Liu et al. "DinoSR: Self-Distillation and Online Clustering for Self-Supervised Speech Representation Learning." Neural Information Processing Systems, 2023.Markdown
[Liu et al. "DinoSR: Self-Distillation and Online Clustering for Self-Supervised Speech Representation Learning." Neural Information Processing Systems, 2023.](https://mlanthology.org/neurips/2023/liu2023neurips-dinosr/)BibTeX
@inproceedings{liu2023neurips-dinosr,
title = {{DinoSR: Self-Distillation and Online Clustering for Self-Supervised Speech Representation Learning}},
author = {Liu, Alexander H. and Chang, Heng-Jui and Auli, Michael and Hsu, Wei-Ning and Glass, Jim},
booktitle = {Neural Information Processing Systems},
year = {2023},
url = {https://mlanthology.org/neurips/2023/liu2023neurips-dinosr/}
}