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In this paper, we report out experiments on NTCIR-11 SpokenDoc&Query task for spoken term detection (STD) and spoken content retrieval (SCR). In STD, we consider acoustic feature similarity between utterances over both word and sub-word lattices to deal with the general problem of open vocabulary retrieval with queries of variable length. In SCR, we modify(More)
IVE HEADLINE GENERATION FOR SPOKEN CONTENT BY ATTENTIVE RECURRENT NEURAL NETWORKS WITH ASR ERROR MODELING Lang-Chi Yu, Hung-yi Lee and Lin-shan Lee Graduate Institute of Communication Engineering, National Taiwan University Graduate Institute of Computer Science and Information Engineering, National Taiwan University {r04942056, hungyilee}@ntu.edu.tw,(More)
A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural network (CNN) model that treats it as a(More)
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