Large-Scale ASR Domain Adaptation Using Self- and Semi-Supervised Learning

Dongseong Hwang; Ananya Misra; Zhouyuan Huo; Nikhil Siddhartha; Shefali Garg; David Qiu; Khe Chai Sim; Trevor Strohman

ICASSP 2022accepted0 citations

Large-Scale ASR Domain Adaptation Using Self- and Semi-Supervised Learning

Dongseong Hwang, Ananya Misra, Zhouyuan Huo, Nikhil Siddhartha, Shefali Garg, David Qiu, Khe Chai Sim, Trevor Strohman

Abstract

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance model performance. However, these approaches mostly focus on in-domain performance for public datasets. In this study, we utilize the combination of self- and semi-supervised learning methods to solve unseen domain adaptation problems in a large-scale production setting for online ASR model. This approach demonstrates that using the source domain data with a small fraction of the target domain data (3%) can recover the performance gap compared to a full data baseline: 13.5% relative WER improvement for target domain data.

BibTeX

@inproceedings{icassp2022_largescaleasrdom,
  title = {Large-Scale ASR Domain Adaptation Using Self- and Semi-Supervised Learning},
  author = {Dongseong Hwang and Ananya Misra and Zhouyuan Huo and Nikhil Siddhartha and Shefali Garg and David Qiu and Khe Chai Sim and Trevor Strohman and Françoise Beaufays and Yanzhang He},
  booktitle = {ICASSP 2022},
  year = {2022}
}