论文标题
改善资源识别资源识别的元学习改进
Improved Meta Learning for Low Resource Speech Recognition
论文作者
论文摘要
我们为低资源语音识别提出了一个新的基于元学习的框架，以改善先前的不可知论元学习（MAML）方法。 MAML是一种简单而强大的元学习方法。但是，MAML呈现出一些核心缺陷，例如训练不稳定性和收敛速度较慢。为了解决这些问题，我们采用多步损失（MSL）。 MSL的目的是在MAML内部环的每个步骤中计算损失，然后将其与加权重要性向量相结合。重要性向量确保了最后一步的损失比以前的步骤更重要。我们的经验评估表明，MSL显着提高了训练程序的稳定性，因此也提高了整体系统的准确性。我们提出的系统在字符错误率和稳定的培训行为方面优于基于MAML的低资源ASR系统。
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.
