This paper presents our latest investigations of the jointly trained maximum entropy and recurrent neural network language models for Code-Switching speech. First, we explore extensively the integration of part-of-speech tags and language identifier information in recurrent neural network language models for CodeSwitching. Second, the importance of the maximum entropy model is demonstrated alon...

Bidirectional recurrent neural network(BRNN) is a noncausal system that captures both upstream and downstream information for protein secondary structure prediction. Due to the problem of vanishing gradients, the BRNN can not learn remote information efficiently. To limit this problem, we propose segmented memory recurrent neural network(SMRNN) and use SMRNNs to replace the standard RNNs in BRN...

Video summarisation can relieve the pressure on video storage, transmission, archiving, and retrieval caused by explosive growth of online videos in recent years. Most existing supervised methods use convolutional neural network (CNN) or recurrent (RNN) to model temporal dependencies between frames shots. CNN mainly focuses local information, RNN loses long-term information when input sequence ...