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• W2894835365 abstract "Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multilingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance comparable to the models trained with twice more training data." @default.
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• W2894835365 date "2018-12-01" @default.
• W2894835365 modified "2023-09-29" @default.
• W2894835365 title "Multilingual Sequence-to-Sequence Speech Recognition: Architecture, Transfer Learning, and Language Modeling" @default.
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• W2894835365 doi "https://doi.org/10.1109/slt.2018.8639655" @default.
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