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Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition
Open accessAbstract
Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.
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Speech recognition with deep recurrent neural networks
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Recurrent neural network based language model
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Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
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Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700
LSTM time and frequency recurrence for automatic speech recognition
Li, J., Mohamed, A., Zweig, G., & Gong, Y. (2015) · 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2015.7404793 · Cited by 119
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Metadata
- Authors
- Li, X., & Wu, X.
- Venue
- 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 4520-4524
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2119615570
- Cited by
- 336
- References
- 6
- FWCI
- 12.12
- Type
- conference paper
- Language
- en
- Open access
- green
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