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• W3167946479 abstract "In this paper, we demonstrate the efficacy of transfer learning and continuous learning for various automatic speech recognition (ASR) tasks using end-to-end models trained with CTC loss. We start with a large pre-trained English ASR model and show that transfer learning can be effectively and easily performed on: (1) different English accents, (2) different languages (from English to German, Spanish, Russian, or from Mandarin to Cantonese) and (3) application-specific domains. Our extensive set of experiments demonstrate that in all three cases, transfer learning from a good base model has higher accuracy than a model trained from scratch. Our results indicate that, for fine-tuning, larger pre-trained models are better than small pre-trained models, even if the dataset for fine-tuning is small. We also show that transfer learning significantly speeds up convergence, which could result in significant cost savings when training with large datasets." @default.
• W3167946479 created "2021-06-22" @default.
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• W3167946479 date "2021-07-05" @default.
• W3167946479 modified "2023-10-02" @default.
• W3167946479 title "Cross-Language Transfer Learning and Domain Adaptation for End-to-End Automatic Speech Recognition" @default.
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• W3167946479 doi "https://doi.org/10.1109/icme51207.2021.9428334" @default.
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