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Long short-term memory (LSTM) based acoustic modeling methods have recentlybeen shown to give state-of-the-art performance on some speech recognitiontasks. To achieve a further performance improvement, in this research, deepextensions on LSTM are investigated considering that deep hierarchical modelhas turned out to be more efficient than a shallow one. Motivated by previousresearch on constructing deep recurrent neural networks (RNNs), alternativedeep LSTM architectures are proposed and empirically evaluated on a largevocabulary conversational telephone speech recognition task. Meanwhile,regarding to multi-GPU devices, the training process for LSTM networks isintroduced and discussed. Experimental results demonstrate that the deep LSTMnetworks benefit from the depth and yield the state-of-the-art performance onthis task.

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