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.
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Project supported by the National Natural Science Foundation of China (Nos. 61272211 and 61170126) and the Six Talent Peaks Foundation of Jiangsu Province, China (No. DZXX027)
ORCID: Zheng-wei HUANG, http://orcid.org/0000-0001-7788-0526; Qi-rong MAO, http://orcid.org/0000-0002-5021-9057
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Huang, Zw., Xue, Wt. & Mao, Qr. Speech emotion recognition with unsupervised feature learning. Frontiers Inf Technol Electronic Eng 16, 358–366 (2015). https://doi.org/10.1631/FITEE.1400323
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DOI: https://doi.org/10.1631/FITEE.1400323