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arXiv · 2005.04290

Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition

Jocelyn Huang·Oleksii Kuchaiev·Patrick O'Neill·Vitaly Lavrukhin·Jason Li·Adriana Flores·Georg Kucsko·Boris Ginsburg
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Abstract

In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks. We start with a pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (German, Spanish and Russian) and (3) application-specific domains. Our experiments demonstrate that in all three cases, transfer learning from a good base model has higher accuracy than a model trained from scratch. It is preferred to fine-tune large models than small pre-trained models, even if the dataset for fine-tuning is small. Moreover, transfer learning significantly speeds up convergence for both very small and very large target datasets.

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Jocelyn Huang, Oleksii Kuchaiev, Patrick O'Neill, Vitaly Lavrukhin, Jason Li, Adriana Flores, Georg Kucsko, Boris Ginsburg. 2020-05-08. Cross-Language Transfer Learning, Continuous Learning, and Domain Adaptation for End-to-End Automatic Speech Recognition. https://arxiv.org/abs/2005.04290

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