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Hybrid Deep Neural Network-Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition Host Publication: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2013) Authors: L. Li, Y. Zhao, D. Jiang, Y. Zhang, F. Wang, I. Gonzalez, V. Enescu and H. Sahli Publisher: IEEE Publication Year: 2013 Number of Pages: 6 ISBN: 978-0-7695-5048-0
Abstract: Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). In this paper, for emotion recognition from speech, we investigate DNN-HMMs with restricted Boltzmann Machine (RBM) based unsupervised pre-training, and DNN-HMMs with discriminative pre-training. Emotion recognition experiments are carried out on these two models on the eNTERFACEཁ database and Berlin database, respectively, and results are compared with those from the GMM-HMMs, the shallow-NN-HMMs with two layers, as well as the Multi-layer Perceptrons HMMs (MLP-HMMs). Experimental results show that when the numbers of the hidden layers as well hidden units are properly set, the DNN could extend the labeling ability of GMM-HMM. Among all the models, the DNN-HMMs with discriminative pre-training obtain the best results. For example, for the eNTERFACEཁ database, the recognition accuracy improves 12.22% from the DNN-HMMs with unsupervised pre-training, 11.67% from the GMM-HMMs, 10.56% from the MLP-HMMs, and even 17.22% from the shallow-NN-HMMs, respectively.
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