IMPROVING LONG SHORT-TERM MEMORY NETWORKS USING MAXOUT UNITS FOR LARGE VOCABULARY SPEECH RECOGNITION

Long short-term memory (LSTM) recurrent neural networks have been shown to give state-of-the-art performance on many speech recognition tasks. To achieve a further performance improvement, in this paper, maxout units are proposed to be integrated with the LSTM cells, considering those units have brought significant improvements to deep feed-forward neural networks. A novel architecture was constructed by replacing the input activation units (generally tanh) in the LSTM networks with maxout units. We implemented the LSTM network training on multi-GPU devices with truncated BPTT, and empirically evaluated the proposed designs on a large vocabulary Mandarin conversational telephone speech recognition task. The experimental results support our claim that the performance of LSTM based acoustic models can be further improved using the maxout units.