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:通过神经网络的多语言语音识别,往往通过批量学习来实现,所有语言在培训前都可以使用,在培训后增加新语言的能力在经济上可能有益,但主要挑战是灾难性的遗忘。在这项工作中,我们结合了重因数和弹性重量整合的素质,以克服灾难性的遗忘,促进快速学习新语言。这种结合使我们能够消除灾难性的遗忘,同时仍然能够实现新语言的功能,与同时拥有所有语言相比,从最初的10种语言中学习26种语言,而不是灾难性的遗忘,并且与从头到尾培训所有语言相比,表现合理。