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; Françoise Beaufays; Yanzhang He

arXiv · 2110.00165

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·Françoise Beaufays·Yanzhang He

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Abstract

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance. However, the approach 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 problem 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: relative 13.5% WER improvement for target domain data.

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BibTeX RIS

Dongseong Hwang, Ananya Misra, Zhouyuan Huo, Nikhil Siddhartha, Shefali Garg, David Qiu, Khe Chai Sim, Trevor Strohman, Françoise Beaufays, Yanzhang He. 2021-10-01. Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning. https://arxiv.org/abs/2110.00165

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