Multilingual speech recognition with neural networks is often implemented with batch-learning, when all of the languages are available before training. An ability to add new languages after the prior training sessions can be economically beneficial, but the main challenge is catastrophic forgetting. In this work, we combine the qualities of weight factorization and elastic weight consolidation in order to counter catastrophic forgetting and facilitate learning new languages quickly. Such combination allowed us to eliminate catastrophic forgetting while still achieving performance for the new languages comparable with having all languages at once, in experiments of learning from an initial 10 languages to achieve 26 languages without catastrophic forgetting and a reasonable performance compared to training all languages from scratch.

翻译：基于神经网络的语音识别系统通常采用批量学习方式实现多语言识别，即所有语言数据均在训练前准备就绪。然而，具备在已有训练基础上持续添加新语言的能力将带来显著的经济效益，其核心挑战在于克服灾难性遗忘问题。本研究通过融合权重分解与弹性权重巩固方法的优势，在有效抑制灾难性遗忘的同时，实现了新语言的快速学习。实验表明，该融合策略不仅完全消除了灾难性遗忘现象，而且使新增语言的识别性能达到与全语言批量训练相当的水平。在从初始10种语言扩展至26种语言的持续学习过程中，系统在避免灾难性遗忘的同时，其性能表现与从头开始训练全部语言的传统方法相比具有可比性。