Full Text:   <5336>

Summary:  <2071>

CLC number: TP391.4

On-line Access: 2015-05-05

Received: 2014-09-16

Revision Accepted: 2015-03-04

Crosschecked: 2015-04-10

Cited: 6

Clicked: 8504

Citations:  Bibtex RefMan EndNote GB/T7714


Qi-rong Mao


Zheng-wei Huang


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.5 P.358-366


Speech emotion recognition with unsupervised feature learning

Author(s):  Zheng-wei Huang, Wen-tao Xue, Qi-rong Mao

Affiliation(s):  Department of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China

Corresponding email(s):   zhengwei.hg@gmail.com, striveyou@163.com, mao_qr@mail.ujs.edu.cn

Key Words:  Speech emotion recognition, Unsupervised feature learning, Neural network, Affect computing

Zheng-wei Huang, Wen-tao Xue, Qi-rong Mao. Speech emotion recognition with unsupervised feature learning[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(5): 358-366.

@article{title="Speech emotion recognition with unsupervised feature learning",
author="Zheng-wei Huang, Wen-tao Xue, Qi-rong Mao",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Speech emotion recognition with unsupervised feature learning
%A Zheng-wei Huang
%A Wen-tao Xue
%A Qi-rong Mao
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 5
%P 358-366
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400323

T1 - Speech emotion recognition with unsupervised feature learning
A1 - Zheng-wei Huang
A1 - Wen-tao Xue
A1 - Qi-rong Mao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 5
SP - 358
EP - 366
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1400323

Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including K-means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

The paper presents a very interesting issue related to unsupervised feature extraction for speech emotion recognition.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Abdel-Hamid, O., Mohamed, A.R., Jiang, H., et al., 2012. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.4277-4280.

[2]Burkhardt, F., Paeschke, A., Rolfes, M., et al., 2005. A database of German emotional speech. Interspeech, p.1517-1520.

[3]Chan, T.H., Jia, K., Gao, S., et al., 2014. PCANet: a simple deep learning baseline for image classification? arXiv preprint, arXiv:1404.3606.

[4]Coates, A., Ng, A.Y., Lee, H., 2011. An analysis of single-layer networks in unsupervised feature learning. Int. Conf. on Artificial Intelligence and Statistics, p.215-223.

[5]Dahl, G.E., Yu, D., Deng, L., et al., 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process., 20(1):30-42.

[6]El Ayadi, M., Kamel, M.S., Karray, F., 2011. Survey on speech emotion recognition: features, classification schemes, and databases. Patt. Recogn., 44(3):572-587.

[7]Feraru, M., Zbancioc, M., 2013. Speech emotion recognition for SROL database using weighted KNN algorithm. Int. Conf. on Electronics, Computers and Artificial Intelligence, p.1-4.

[8]Gao, H., Chen, S.G., An, P., et al., 2012. Emotion recognition of Mandarin speech for different speech corpora based on nonlinear features. IEEE 11th Int. Conf. on Signal Processing, p.567-570.

[9]Gunes, H., Schuller, B., 2013. Categorical and dimensional affect analysis in continuous input: current trends and future directions. Image Vis. Comput., 31(2):120-136.

[10]Haq, S., Jackson, P.J., 2009. Speaker-dependent audio-visual emotion recognition. Auditory-Visual Speech Processing, p.53-58.

[11]Hinton, G., Deng, L., Yu, D., et al., 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag., 29(6):82-97.

[12]Kim, Y., Lee, H., Provost, E.M., 2013. Deep learning for robust feature generation in audiovisual emotion recognition. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.3687-3691.

[13]Koolagudi, S.G., Devliyal, S., Barthwal, A., et al., 2012. Emotion recognition from semi natural speech using artificial neural networks and excitation source features. In: Contemporary Computing. Springer Berlin Heidelberg, p.273-282.

[14]Le, D., Provost, E.M., 2013. Emotion recognition from spontaneous speech using hidden Markov models with deep belief networks. IEEE Workshop on Automatic Speech Recognition and Understanding, p.216-221.

[15]Lee, H., Pham, P., Largman, Y., et al., 2009. Unsupervised feature learning for audio classification using convolutional deep belief networks. Advances in Neural Information Processing Systems, p.1096-1104.

[16]Li, L., Zhao, Y., Jiang, D., et al., 2013. Hybrid deep neural network–hidden Markov model (DNN-HMM) based speech emotion recognition. Humaine Association Conf. on Affective Computing and Intelligent Interaction, p.312-317.

[17]Mao, Q., Wang, X., Zhan, Y., 2010. Speech emotion recognition method based on improved decision tree and layered feature selection. Int. J. Human. Robot., 7(2):245-261.

[18]Mao, Q.R., Zhao, X.L., Huang, Z.W., et al., 2013. Speaker-independent speech emotion recognition by fusion of functional and accompanying paralanguage features. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 14(7):573-582.

[19]Martin, O., Kotsia, I., Macq, B., et al., 2006. The eNTERFACE’05 audio-visual emotion database. Proc. Int. Conf. on Data Engineering Workshops, p.8.

[20]Mencattini, A., Martinelli, E., Costantini, G., et al., 2014. Speech emotion recognition using amplitude modulation parameters and a combined feature selection procedure. Knowl.-Based Syst., 63:68-81.

[21]Mohamed, A.R., Dahl, G.E., Hinton, G., 2012. Acoustic modeling using deep belief networks. IEEE Trans. Audio Speech Lang. Process., 20(1):14-22.

[22]Nicolaou, M.A., Gunes, H., Pantic, M., 2011. Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space. IEEE Trans. Affect. Comput., 2(2):92-105.

[23]Pantic, M., Nijholt, A., Pentland, A., et al., 2008. Human-centred intelligent human? Computer interaction (HCI2): how far are we from attaining it? Int. J. Auton. Adapt. Commun. Syst., 1(2):168-187.

[24]Ramakrishnan, S., El Emary, I.M., 2013. Speech emotion recognition approaches in human computer interaction. Telecommun. Syst., 52(3):1467-1478.

[25]Ranzato, M., Huang, F.J., Boureau, Y.L., et al., 2007. Unsupervised learning of invariant feature hierarchies with applications to object recognition. IEEE Conf. on Computer Vision and Pattern Recognition, p.1-8.

[26]Razavian, A.S., Azizpour, H., Sullivan, J., et al., 2014. CNN features off-the-shelf: an astounding baseline for recognition. arXiv preprint, arXiv:1403.6382.

[27]Schmidt, E.M., Kim, Y.E., 2011. Learning emotion-based acoustic features with deep belief networks. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, p.65-68.

[28]Stuhlsatz, A., Meyer, C., Eyben, F., et al., 2011. Deep neural networks for acoustic emotion recognition: raising the benchmarks. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.5688-5691.

[29]Sun, R., Moore, E.II, 2011. Investigating glottal parameters and Teager energy operators in emotion recognition. LNCS, 6975:425-434.

[30]Sun, Y., Wang, X., Tang, X., 2013. Deep learning face representation from predicting 10,000 classes. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1891-1898.

[31]Thapliyal, N., Amoli, G., 2012. Speech based emotion recognition with Gaussian mixture model. Int. J. Adv. Res. Comput. Eng. Technol., 1(5):65-69.

[32]Wu, C.H., Liang, W.B., 2011. Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels. IEEE Trans. Affect. Comput., 2(1):10-21.

[33]Wu, S., Falk, T.H., Chan, W.Y., 2011. Automatic speech emotion recognition using modulation spectral features. Speech Commun., 53(5):768-785.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE