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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2014 - 3

 HIGH-IMPACT PAPER 

Graph Learning Based Speaker Independent Speech Emotion Recognition

XU, X. See more information about XU, X. on SCOPUS See more information about XU, X. on IEEExplore See more information about XU, X. on Web of Science, HUANG, C. See more information about  HUANG, C. on SCOPUS See more information about  HUANG, C. on SCOPUS See more information about HUANG, C. on Web of Science, WU, C. See more information about  WU, C. on SCOPUS See more information about  WU, C. on SCOPUS See more information about WU, C. on Web of Science, WANG, Q. See more information about  WANG, Q. on SCOPUS See more information about  WANG, Q. on SCOPUS See more information about WANG, Q. on Web of Science, ZHAO, L. See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on Web of Science
 
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Download PDF pdficon (729 KB) | Citation | Downloads: 1,288 | Views: 4,414

Author keywords
speech emotion recognition, speaker penalty graph learning, graph embedding framework, dimensionality reduction

References keywords
recognition(12), speech(10), emotion(8), analysis(8), pattern(7), reduction(5), human(5), dimensionality(5), science(4), machine(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-05-31
Volume 14, Issue 2, Year 2014, On page(s): 17 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.02003
Web of Science Accession Number: 000340868100003
SCOPUS ID: 84901856862

Abstract
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In this paper, the algorithm based on graph learning and graph embedding framework, Speaker-Penalty Graph Learning (SPGL), is proposed in the research of speech emotion recognition to solve the problems caused by different speakers. Graph embedding framework theory is used to construct the dimensionality reduction stage of speech emotion recognition. Special penalty and intrinsic graphs of the graph embedding framework is proposed to penalize the impacts from different speakers in the task of speech emotion recognition. The original speech emotion features are extracted by various categories, reflecting different characteristics of each speech sample. According to the experiments in speech emotion corpus using different classifiers, the proposed method with linear and kernelized mapping forms can both achieve relatively better performance than the state-of-the-art dimensionality reduction methods.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


[2] D. Ververidis, C. Kotropoulos, "Emotional speech recognition: Resources, features, and methods," Speech Communication, vol. ED-48, pp. 1162-1181, 2006.
[CrossRef] [Web of Science Times Cited 558] [SCOPUS Times Cited 745]


[3] B. Schuller, G. Rigoll, "Timing levels in segment-based speech emotion recognition," in INTERSPEECH'2006, Pittsburgh, PA, USA, 2006, pp. 1818-1821.

[4] P. Oudeyer, "The production and recognition of emotions in speech: features and algorithms," International Journal of Human-Computer Studies, vol. ED-59, pp. 157-183, 2003.
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[5] R. Tato, R. Santos, R. Kompe, J. Pardo, "Emotional space improves emotion recognition," in International Conference on Spoken Language, Denver, CO, USA, 2002, pp. 2029-2032.

[6] B. Schuller, R. Müller, M. K. Lang, G. Rigoll, "Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles," in INTERSPEECH'2005, Lisbon, Portugal, 2005, pp. 805-808.

[7] B. Schuller, S. Reiter, R. Muller, M. Al-Hames, "Speaker independent speech emotion recognition by ensemble classification," in IEEE International Conf. Multimedia and Expo(ICME), Amsterdam, The Netherlands, 2005, pp. 864-867.
[CrossRef] [SCOPUS Times Cited 132]


[8] T. Kostoulas, T. Ganchev, N. Fakotakis, "Study on speaker-independent emotion recognition from speech on real-world data," in Verbal and nonverbal features of human-human and human-machine interaction, Springer Berlin Heidelberg, 2008, pp. 235-242.
[CrossRef] [SCOPUS Times Cited 13]


[9] M. Belkin, P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," in Advances in Neutral Information Processing Systems(NIPS) 14, Vancouver, Canada, 2002, pp. 585-591.

[10] X. He, P. Niyogi, "Locality preserving projections," in Advances in Neural Information Processing Systems (NIPS) 16, Whistler, Canada, 2003, pp. 153-160.

[11] S. Roweis, L. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. ED-290(5500), pp. 2323-2326, 2000.
[CrossRef] [Web of Science Times Cited 10315] [SCOPUS Times Cited 12963]


[12] S. Lafon, A. Lee, "Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. ED-28(9), pp. 1393-1403, 2006.
[CrossRef] [Web of Science Times Cited 402] [SCOPUS Times Cited 495]


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[CrossRef] [SCOPUS Times Cited 600]


[15] S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, S. Lin, "Graph embedding and extensions: a general framework for dimensionality reduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. ED-29(1), pp. 40-51, 2007.
[CrossRef] [Web of Science Times Cited 2376] [SCOPUS Times Cited 2876]


[16] F. De la Torre, "A least-squares framework for component analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. ED-34(6), pp. 1041-1055, 2012.
[CrossRef] [Web of Science Times Cited 127] [SCOPUS Times Cited 150]


[17] M. You, C. Chen, J. Bu, J. Liu, J. Tao, "Emotional speech analysis on nonlinear manifold," in International Conference on Pattern Recognition(ICPR), Hong Kong, 2006, pp. 91-94.
[CrossRef] [SCOPUS Times Cited 24]


[18] S. Zhang, X. Zhao, B. Lei, "Speech emotion recognition using an enhanced Kernel Isomap for human-robot interaction," International Journal of Advanced Robotic Systems, vol. ED-10(114), pp. 1-7, 2013.
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[19] J. Shawe-Taylor, N. Cristianini, Kernel methods for pattern analysis. Cambridge University Press, 2004.

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[CrossRef] [SCOPUS Times Cited 697]


[22] L. He, J. M. Buenaposada, L. Baumela, "An empirical comparison of graph-based dimensionality reduction algorithms on facial expression recognition tasks," in International Conf. Pattern Recognition (ICPR), Tampa, FL, USA, 2008, pp. 1-4.
[CrossRef]


[23] F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier, B. Weiss, "A database of German emotional speech," in INTERSPEECH'2005, Lisbon, Portugal, 2005, pp. 1517-1520.

[24] O. Martin, I. Kotsia, B. Macq, I. Pitas, "The enterface'05 audio-visual emotion database," in IEEE Conf. Data Engineering Workshops, Atlanta, GA, USA, 2006, pp. 8-8.
[CrossRef] [SCOPUS Times Cited 560]




References Weight

Web of Science® Citations for all references: 22,922 TCR
SCOPUS® Citations for all references: 32,916 TCR

Web of Science® Average Citations per reference: 917 ACR
SCOPUS® Average Citations per reference: 1,317 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-11-06 07:15 in 116 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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