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.
翻译:我们为低资源语音识别提出了一个新的元学习框架,改进了先前的模型不可知元学习(MAML)方法。MAML是一个简单而有力的元学习方法。然而,MAML呈现了一些核心缺陷,如培训不稳定性和趋同速度慢。为了解决这些问题,我们采用了多步损失(MSL)。MSL的目的是计算MAML内部循环的每一步的损失,然后将其与加权重要性矢量结合起来。重要的矢量确保最后一步的损失比以往步骤更重要。我们的经验评估表明,MSL大大改善了培训程序的稳定性,从而也提高了整个系统的准确性。我们提议的系统在性格错误率和稳定培训行为方面,以各种语言为基础,在低资源ASR系统上优于MAML。