The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Speech emotion recognition using deep neural network and extreme learning machine
Abstract
Speech emotion recognition is a challenging problem partly because it is unclear what features are effective for the task. In this paper we propose to utilize deep neural networks (DNNs) to extract high level features from raw data and show that they are effective for speech emotion recognition. We first produce an emotion state probability distribution for each speech segment using DNNs. We then construct utterance-level features from segment-level probability distributions. These utterancelevel features are then fed into an extreme learning machine (ELM), a special simple and efficient single-hidden-layer neural network, to identify utterance-level emotions. The experimental results demonstrate that the proposed approach effectively learns emotional information from low-level features and leads to 20% relative accuracy improvement compared to the stateof-the-art approaches.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Self-citationRecent advances in deep learning for speech research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639345 · Cited by 801
Self-citationOpenEAR — Introducing the munich open-source emotion and affect recognition toolkit
Eyben, F., Wollmer, M., & Schuller, B. (2009) · 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops · doi:10.1109/acii.2009.5349350 · Cited by 418
Emotion Recognition based on Phoneme Classes
Lee, C. M., Yildirim, S., Bulut, M., Kazemzadeh, A., Busso, C., Deng, Z., Lee, S., & Narayanan, S. (2004) · Interspeech 2004 · doi:10.21437/interspeech.2004-322 · Cited by 228
A Framework for Automatic Human Emotion Classification Using Emotion Profiles
Mower, E., Mataric, M. J., & Narayanan, S. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2076804 · Cited by 215
Cited by
A review of affective computing: From unimodal analysis to multimodal fusion
Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.02.003 · Cited by 1,644
Speech emotion recognition using deep 1D & 2D CNN LSTM networks
Zhao, J., Mao, X., & Chen, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.035 · Cited by 1,044
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv · doi:10.48550/arxiv.1704.08619 · Cited by 752
Automatic speech emotion recognition using recurrent neural networks with local attention
Mirsamadi, S., Barsoum, E., & Zhang, C. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952552 · Cited by 770
Speech Emotion Recognition Using Deep Learning Techniques: A Review
Khalil, R. A., Jones, E., Babar, M. I., Jan, T., Zafar, M. H., & Alhussain, T. (2019) · IEEE Access · doi:10.1109/access.2019.2936124 · Cited by 702
Metadata
- Authors
- Han, K., Yu, D., & Tashev, I.
- Venue
- Interspeech 2014
- Publisher
- ISCA
- Published
- 2014-09-14
- Pages
- 223-227
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2295001676
- Cited by
- 794
- References
- 6
- FWCI
- 59.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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