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 vector. The importance vector
ensures that the loss at the last step has more importance than the previous
steps. Our empirical evaluation shows that MSL significantly improves the
stability of the training procedure and it thus also improves the accuracy of
the overall system. Our proposed system outperforms MAML based low resource ASR
system on various languages in terms of character error rates and stable
training behavior.

提出了一种基于元学习的新框架来改进之前的模型无关元学习（MAML）方法，该方法采用多步损失（MSL）来解决 MAML 方法的稳定性问题，其实验表明 MSL 显著提高了训练过程的稳定性和整个系统的准确性，优于各种语言的 MAML 低资源 ASR 系统的字符错误率和稳定性。