Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Question detection is of importance for many speech applications. Only parts of the speech utterances can provide useful clues for question detection. Previous work of question detection using acoustic features in Mandarin conversation is ak in capturing such proper time context information, which could be modeled essentially in recurrent neural network (RNN) structure. ,  conduct an investigation on recurrent approaches to cope with this problem. Based on gated recurrent unit (GRU),  build different RNN and bidirectional RNN (BRNN) models to extract efficient features at segment and utterance level. The particular advantage of GRU is it can determine a proper time scale to extract high-level contextual features. Experimental results show that the features extracted within proper time scale make the classifier perform better than the baseline method with pre-designed lexical and acoustic feature set.
['Yaodong Tang', 'Yuchen Huang', 'Zhiyong Wu', 'Helen M. Meng', 'Mingxing Xu', 'Lianhong Cai']
I am writing a research paper on a idea Question detection from acoustic features using recurrent neural network with gated recurrent unit
[{"from": "human", "value": "I am writing a research paper on a idea Question detection from acoustic features using recurrent neural network with gated recurrent unit"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Question detection is of importance for many speech applications. Only parts of the speech utterances can provide useful clues for question detection. Previous work of question detection using acoustic features in Mandarin conversation is ak in capturing such proper time context information, which could be modeled essentially in recurrent neural network (RNN) structure. ,  conduct an investigation on recurrent approaches to cope with this problem. Based on gated recurrent unit (GRU),  build different RNN and bidirectional RNN (BRNN) models to extract efficient features at segment and utterance level. The particular advantage of GRU is it can determine a proper time scale to extract high-level contextual features. Experimental results show that the features extracted within proper time scale make the classifier perform better than the baseline method with pre-designed lexical and acoustic feature set."}]