Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning

• URL: http://arxiv.org/abs/2110.00165v2
• Date: Mon, 4 Oct 2021 23:40:03 GMT
• Title: Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning
• Authors: Dongseong Hwang, Ananya Misra, Zhouyuan Huo, Nikhil Siddhartha, Shefali Garg, David Qiu, Khe Chai Sim, Trevor Strohman, Fran\c{c}oise Beaufays, Yanzhang He
• Abstract summary: 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.
• Score: 26.110250680951854
• License: http://creativecommons.org/licenses/by/4.0/
• 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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