메뉴 건너뛰기




Volumn , Issue , 2014, Pages 223-227

Speech emotion recognition using deep neural network and extreme learning machine

Author keywords

Deep neural networks; Emotion recognition; Extreme learning machine

Indexed keywords

KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; NETWORK LAYERS; PROBABILITY DISTRIBUTIONS; SPEECH; SPEECH COMMUNICATION;

EID: 84910060363     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (731)

References (22)
  • 1
    • 78649328053 scopus 로고    scopus 로고
    • Survey on speech emotion recognition: Features, classification schemes, and databases
    • M. El Ayadi, M. S. Kamel, and F. Karray, "Survey on speech emotion recognition: Features, classification schemes, and databases, " Pattern Recognition, vol. 44, no. 3, pp. 572-587, 2011.
    • (2011) Pattern Recognition , vol.44 , Issue.3 , pp. 572-587
    • El Ayadi, M.1    Kamel, M.S.2    Karray, F.3
  • 2
    • 79960846940 scopus 로고    scopus 로고
    • Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge
    • B. Schuller, A. Batliner, S. Steidl, and D. Seppi, "Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge, " Speech Communication, vol. 53, no. 9, pp. 1062-1087, 2011.
    • (2011) Speech Communication , vol.53 , Issue.9 , pp. 1062-1087
    • Schuller, B.1    Batliner, A.2    Steidl, S.3    Seppi, D.4
  • 3
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets, " Neural Computation, vol. 18, no. 7, pp. 1527-1554, 2006.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 5
  • 6
    • 33745903481 scopus 로고    scopus 로고
    • Extreme learning machine: Theory and applications
    • G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications, " Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006.
    • (2006) Neurocomputing , vol.70 , Issue.1 , pp. 489-501
    • Huang, G.-B.1    Zhu, Q.-Y.2    Siew, C.-K.3
  • 7
    • 0141478857 scopus 로고    scopus 로고
    • Hidden markov model-based speech emotion recognition
    • IEEE
    • B. Schuller, G. Rigoll, and M. Lang, "Hidden markov model-based speech emotion recognition, " in Proceedings of IEEE ICASSP 2003, vol. 2. IEEE, 2003, pp. II-1.
    • (2003) Proceedings of IEEE ICASSP 2003 , vol.2 , pp. II-1
    • Schuller, B.1    Rigoll, G.2    Lang, M.3
  • 9
    • 34547507232 scopus 로고    scopus 로고
    • Gmm supervector based SVM with spectral features for speech emotion recognition
    • IEEE
    • H. Hu, M.-X. Xu, and W. Wu, "GMM supervector based SVM with spectral features for speech emotion recognition, " in Proceedings of IEEE ICASSP 2007, vol. 4. IEEE, 2007, pp. IV-413.
    • (2007) Proceedings of IEEE ICASSP 2007 , vol.4 , pp. IV-413
    • Hu, H.1    Xu, M.-X.2    Wu, W.3
  • 10
    • 84890540010 scopus 로고    scopus 로고
    • Bhattacharyya distance based emotional dissimilarity measure for emotion classification
    • IEEE
    • T. L. Nwe, N. T. Hieu, and D. K. Limbu, "Bhattacharyya distance based emotional dissimilarity measure for emotion classification, " in Proceedings of IEEE ICASSP 2013. IEEE, 2013, pp. 7512-7516.
    • (2013) Proceedings of IEEE ICASSP 2013 , pp. 7512-7516
    • Nwe, T.L.1    Hieu, N.T.2    Limbu, D.K.3
  • 11
    • 77949415384 scopus 로고    scopus 로고
    • OpenEAR - introducing the Munich open-source emotion and affect recognition toolkit
    • IEEE
    • F. Eyben, M. Wollmer, and B. Schuller, "OpenEAR - introducing the Munich open-source emotion and affect recognition toolkit, " in Proceedings of ACII 2009. IEEE, 2009, pp. 1-6.
    • (2009) Proceedings of ACII 2009 , pp. 1-6
    • Eyben, F.1    Wollmer, M.2    Schuller, B.3
  • 14
    • 84890515467 scopus 로고    scopus 로고
    • Emotion classification via utterance-level dynamics: A pattern-based approach to characterizing affective expressions
    • IEEE
    • Y. Kim and E. Mower Provost, "Emotion classification via utterance-level dynamics: A pattern-based approach to characterizing affective expressions, " in Proceedings of IEEE ICASSP 2013. IEEE, 2013.
    • (2013) Proceedings of IEEE ICASSP 2013
    • Kim, Y.1    Provost, E.M.2
  • 16
    • 84874282188 scopus 로고    scopus 로고
    • Improving wideband speech recognition using mixed-bandwidth training data in CD-DNN-HMM
    • J. Li, D. Yu, J.-T. Huang, and Y. Gong, "Improving wideband speech recognition using mixed-bandwidth training data in CD-DNN-HMM, " in Proceedings of SLT, 2012.
    • (2012) Proceedings of SLT
    • Li, J.1    Yu, D.2    Huang, J.-T.3    Gong, Y.4
  • 18
    • 84862822032 scopus 로고    scopus 로고
    • Efficient and effective algorithms for training single-hidden-layer neural networks
    • D. Yu and L. Deng, "Efficient and effective algorithms for training single-hidden-layer neural networks, " Pattern Recognition Letters, vol. 33, no. 5, pp. 554-558, 2012.
    • (2012) Pattern Recognition Letters , vol.33 , Issue.5 , pp. 554-558
    • Yu, D.1    Deng, L.2
  • 20
    • 80051611848 scopus 로고    scopus 로고
    • Iterative feature normalization for emotional speech detection
    • IEEE
    • C. Busso, A. Metallinou, and S. S. Narayanan, "Iterative feature normalization for emotional speech detection, " in Proceedings of IEEE ICASSP 2011. IEEE, 2011, pp. 5692-5695.
    • (2011) Proceedings of IEEE ICASSP 2011 , pp. 5692-5695
    • Busso, C.1    Metallinou, A.2    Narayanan, S.S.3
  • 21
    • 84890510905 scopus 로고    scopus 로고
    • Identifying salient sub-utterance emotion dynamics using flexible units and estimates of affective flow
    • IEEE
    • E. Mower Provost, "Identifying salient sub-utterance emotion dynamics using flexible units and estimates of affective flow, " in Proceedings of IEEE ICASSP 2013. IEEE, 2013.
    • (2013) Proceedings of IEEE ICASSP 2013
    • Provost, E.M.1
  • 22


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.