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Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

Xiangang Li, Xihong Wu
Speech and Hearing Research Center, Key Laboratory of Machine Perception (Ministry of Education), Peking University, Beijing, 100871
arXiv:1410.4281 [cs.CL], (16 Oct 2014)

@article{2014arXiv1410.4281L,

   author={Li}, X. and {Wu}, X.},

   title={"{Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1410.4281},

   primaryClass={"cs.CL"},

   keywords={Computer Science – Computation and Language, Computer Science – Neural and Evolutionary Computing},

   year={2014},

   month={oct},

   adsurl={http://adsabs.harvard.edu/abs/2014arXiv1410.4281L},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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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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