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LSTM based language models are an important part of modern LVCSR systems as they significantly improve performance over traditional backoff language models.
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X. Chen, X. Liu, M. J. F. Gales, and P. C. Woodland, “Recurrent neural network language model training with noise contrastive estimation for speech recognition,” in
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2015
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V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an asr corpus based on public domain audio books,” in
2015
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X. Liu, X. Chen, Y. Wang, M. J. F. Gales, and P. C. Woodland, “Two efficient lattice rescoring methods using recurrent neural network language models,”
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2017
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H. Xu, T. Chen, D. Gao, Y. Wang, K. Li, N. Goel, Y. Carmiel, D. Povey, and S. Khudanpur, “A pruned rnnlm lattice-rescoring algorithm for automatic speech recognition,” in
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C. Lüscher, E. Beck, K. Irie, M. Kitza, W. Michel, A. Zeyer, R. Schlüter, and H. Ney, “Rwth asr systems for librispeech: Hybrid vs attention,” in
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