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: 9464
Citations: Bibtex RefMan EndNote GB/T7714
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",
volume="16",
number="5",
pages="358-366",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400323"
}
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400323
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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
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SP - 358
EP - 366
%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1400323
Abstract: 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.
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