random deep belief networks for recognizing emotions from speech signals

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ID: 189722
2017
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Ranked #24 of 77 articles by views in Organic Chemistry Frontiers

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Abstract
Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.
Reference Key
wen2017computationalrandom Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Guihua Wen;Huihui Li;Jubing Huang;Danyang Li;Eryang Xun
Journal Organic Chemistry Frontiers
Year 2017
DOI
10.1155/2017/1945630
URL
http://dx.doi.org/10.1155/2017/1945630
Keywords
computer applications to medicine medical informaticsneurosciences neuropsychiatry biological psychiatry

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