Robust Speech Emotion Recognition with Novel Sub-Band Spectral Centroid Weighted Wavelet Packet Feature

Guo Bao Zhang; Yue Li; Yong Ming Huang

doi:10.4028/www.scientific.net/AMM.610.283

Paper Titles

On-Line Handwriting Recognition Based on Hopfield Neural Network
p.265

Synchronization for Stochastic Complex Networks with Time Varying Delayed and No-Delayed Couping
p.270

The Optimization Selection of Input Variables for Mid-Term Power Load Forecasting Based on Gradually Similar Method
p.274

Detection of Organic Pollutions Using Artificial Neural Networks and Domain Transform Techniques
p.279

Robust Speech Emotion Recognition with Novel Sub-Band Spectral Centroid Weighted Wavelet Packet Feature
p.283

Automatic Counting System for Nuclear Track Based on DSP and Mathematical Morphology Algorithm
p.287

Efficiently Mining Maximal Frequent Itemsets Based on Digraph
p.291

An Inspection Method of SMD Component Type Based on Moment Features and BP Neural Network
p.296

Hybrid Clustering Algorithm Based on KNN and MCL
p.302

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 610Robust Speech Emotion Recognition with Novel...

Robust Speech Emotion Recognition with Novel Sub-Band Spectral Centroid Weighted Wavelet Packet Feature

Abstract:

In this paper, we propose novel sub-band spectral centroid weighted wavelet packet cepstral coefficients (W-WPCC) for noise-robust speech emotion recognition. Experimental results show that the W-WPCC feature demonstrates better noise-robustness in noisy environments.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 610)

Pages:

283-286

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.610.283

Citation:

Cite this paper

Online since:

August 2014

Authors:

Guo Bao Zhang, Yue Li*, Yong Ming Huang

Keywords:

Noise Robustness, Speech Emotion Recognition, Sub-Band Spectral Centroid, Wavelet Packet

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] A.I. Iliev, M.S. Scordilis, Spoken emotion recognition using glottal symmetry, Eurasip Journal on Advances in Signal Processing, 24 (2010) 445-460.

DOI: 10.1155/2011/624575

Google Scholar

[2] M. You, C. Chen, J. Bu, J. Liu, J. Tao, Emotion recognition from noisy speech, Multimedia and Expo, 2006 IEEE International Conference on, (2006) 1653-1656.

DOI: 10.1109/icme.2006.262865

Google Scholar

[3] H. Fastl, E. Zwicer, Psychoacoustics: Facts and Models, 2nd ed., Springer-Verlag, New York, (1999).

Google Scholar

[4] R.C. Ronald, M. Yves, W. Victor, Wavelet analysis and signal processing, In Wavelets and their Applications, (1992) 153-178.

Google Scholar

[5] S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed., Academic Press, Burlington, (2009).

Google Scholar

[6] Z.H. Zeng, J.L. Tu, B.M. Pianfetti, T.S. Huang, Audio-visual affective expression recognition through multistream fused HMM, IEEE Transactions on Multimedia, 10 (2008) 570-577.

DOI: 10.1109/tmm.2008.921737

Google Scholar

[7] K.K. Paliwal, Spectral subband centroid features for speech recognition, in Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on, 2 (1998) 617-620.

DOI: 10.1109/icassp.1998.675340

Google Scholar

[8] A. Karmakar, A. Kumar, R.K. Patney, Design of optimal wavelet packet trees based on auditory perception criterion, Signal Processing Letters, IEEE, 14 (2007) 240-243.

DOI: 10.1109/lsp.2006.884129

Google Scholar

[9] E. Parzen, Autoregressive spectral estimation, in: D.R. Brillinger, P.R. Krishnaiah (Eds. ), Handbook of Statistics, Elsevier, 1983, vol. 3, pp.221-247.

Google Scholar

[10] F. Burkhardt, A. Paeschke, M. Rolfes, et al., A database of German emotional speech, in Interspeech, (2005) 1517-1520.

DOI: 10.21437/interspeech.2005-446

Google Scholar