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