ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Towards Lifelong Learning of End-to-End ASR

Heng-Jui Chang, Hung-yi Lee, Lin-shan Lee

Automatic speech recognition (ASR) technologies today are primarily optimized for given datasets; thus, any changes in the application environment (e.g., acoustic conditions or topic domains) may inevitably degrade the performance. We can collect new data describing the new environment and fine-tune the system, but this naturally leads to higher error rates for the earlier datasets, referred to as catastrophic forgetting. The concept of lifelong learning (LLL) aiming to enable a machine to sequentially learn new tasks from new datasets describing the changing real world without forgetting the previously learned knowledge is thus brought to attention. This paper reports, to our knowledge, the first effort to extensively consider and analyze the use of various approaches of LLL in end-to-end (E2E) ASR, including proposing novel methods in saving data for past domains to mitigate the catastrophic forgetting problem. An overall relative reduction of 28.7% in WER was achieved compared to the fine-tuning baseline when sequentially learning on three very different benchmark corpora. This can be the first step toward the highly desired ASR technologies capable of synchronizing with the continuously changing real world.

doi: 10.21437/Interspeech.2021-563

Cite as: Chang, H.-J., Lee, H.-y., Lee, L.-s. (2021) Towards Lifelong Learning of End-to-End ASR. Proc. Interspeech 2021, 2551-2555, doi: 10.21437/Interspeech.2021-563

  author={Heng-Jui Chang and Hung-yi Lee and Lin-shan Lee},
  title={{Towards Lifelong Learning of End-to-End ASR}},
  booktitle={Proc. Interspeech 2021},