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Computational Intelligence and Neuroscience
Volume 2017, Article ID 1945630, 9 pages
https://doi.org/10.1155/2017/1945630
Research Article

Random Deep Belief Networks for Recognizing Emotions from Speech Signals

School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Correspondence should be addressed to Guihua Wen; nc.ude.tucs@newhgrc

Received 12 October 2016; Revised 21 January 2017; Accepted 6 February 2017; Published 5 March 2017

Academic Editor: Jens Christian Claussen

Copyright © 2017 Guihua Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.