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：多语言语音识别通常采用批量学习的方式实现，即在训练前所有语言均已可用。在先前训练阶段结束后添加新语言的能力可能具有经济价值，但主要挑战在于灾难性遗忘。本研究将权重分解与弹性权重巩固的特性相结合，以应对灾难性遗忘并促进新语言的快速学习。在从初始10种语言扩展至26种语言的实验中，该组合方法不仅消除了灾难性遗忘，还实现了与一次性学习所有语言相媲美的新语言性能，且相对于从头训练所有语言保持了合理表现。