Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual
  Speech Recognition   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.

15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga