Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual\n Speech Recognition
Journal: arXiv (Cornell University)
Year: 2020

One crucial challenge of real-world multilingual speech recognition is the\nlong-tailed distribution problem, where some resource-rich languages like\nEnglish have abundant training data, but a long tail of low-resource languages\nhave varying amounts of limited training data. To overcome the long-tail\nproblem, in this paper, we propose Adapt-and-Adjust (A2), a transformer-based\nmulti-task learning framework for end-to-end multilingual speech recognition.\nThe A2 framework overcomes the long-t