Improved Meta Learning for Low Resource Speech Recognition. (arXiv:2205.06182v1 [cs.CL])

Web: http://arxiv.org/abs/2205.06182

We propose a new meta learning based framework for low resource speech
recognition that improves the previous model agnostic meta learning (MAML)
approach. The MAML is a simple yet powerful meta learning approach. However,
the MAML presents some core deficiencies such as training instabilities and
slower convergence speed. To address these issues, we adopt multi-step loss
(MSL). The MSL aims to calculate losses at every step of the inner loop of MAML
and then combines them with a weighted importance …

arxiv learning meta speech speech recognition