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Learning a set of tasks in sequence remains a challenge for artificial neural networks, which, in such scenarios, tend to suffer from Catastrophic Forgetting (CF).
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“A continual learning survey: Defying forgetting in classification tasks,”
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Heng-Jui Chang, Hung yi Lee, and Lin shan Lee, · 2021
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“Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition,”
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Samik Sadhu and Hynek Hermansky, · 2020
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Brady Houston and Katrin Kirchhoff, · 2020
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“wav2vec 2.0: A framework for self-supervised learning of speech representations,”
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“Conformer: Convolution-augmented Transformer for Speech Recognition,”
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“Layer-wise fast adaptation for end-to-end multi-accent speech recognition,”
Xun Gong, Yizhou Lu, Zhikai Zhou, and Yanmin Qian, · 2021
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“Continual learning for monolingual end-to-end automatic speech recognition,”
Steven Vander Eeckt and Hugo Van hamme, · 2022
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“Exploiting adapters for cross-lingual low-resource speech recognition,”
Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang, Tao Qin, Renjun Xu, and Takahiro Shinozaki, · 2022
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“Efficient adapter transfer of self-supervised speech models for automatic speech recognition,” 2022
Bethan Thomas, Samuel Kessler, and Salah Karout, · 2022
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