Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition

• URL: http://arxiv.org/abs/2012.01687v1
• Date: Thu, 3 Dec 2020 03:46:16 GMT
• Title: Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition
• Authors: Genta Indra Winata, Guangsen Wang, Caiming Xiong, Steven Hoi
• Abstract summary: We propose Adapt-and-Adjust (A2), a transformer-based multi-task learning framework for end-to-end multilingual speech recognition. The A2 framework overcomes the long-tail problem via three techniques: (1) exploiting a pretrained multilingual language model (mBERT) to improve the performance of low-resource languages; (2) proposing dual adapters consisting of both language-specific and language-agnostic adaptation with minimal additional parameters; and (3) overcoming the class imbalance, either by imposing class priors in the loss during training or adjusting the logits of the softmax output during inference.
• Score: 58.849768879796905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One crucial challenge of real-world multilingual speech recognition is the long-tailed distribution problem, where some resource-rich languages like English have abundant training data, but a long tail of low-resource languages have varying amounts of limited training data. To overcome the long-tail problem, in this paper, we propose Adapt-and-Adjust (A2), a transformer-based multi-task learning framework for end-to-end multilingual speech recognition. The A2 framework overcomes the long-tail problem via three techniques: (1) exploiting a pretrained multilingual language model (mBERT) to improve the performance of low-resource languages; (2) proposing dual adapters consisting of both language-specific and language-agnostic adaptation with minimal additional parameters; and (3) overcoming the class imbalance, either by imposing class priors in the loss during training or adjusting the logits of the softmax output during inference. Extensive experiments on the CommonVoice corpus show that A2 significantly outperforms conventional approaches.

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