Joint subspace learning and feature selection method for speech emotion recognition

SONG Peng; ZHENG Wenming; ZHAO Li

doi:10.16511/j.cnki.qhdxxb.2018.26.014

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Journal of Tsinghua University(Science and Technology) ›› 2018, Vol. 58 ›› Issue (4) : 347-351. DOI: 10.16511/j.cnki.qhdxxb.2018.26.014

COMPUTER SCIENCE AND TECHNOLOGY

Joint subspace learning and feature selection method for speech emotion recognition

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Abstract

Traditional speech emotion recognition methods are trained and evaluated on a single corpus. However, when the training and testing use different corpora, the recognition performance drops drastically. A joint subspace learning and feature selection method is presented here to imprive recognition. In this method, the feature subspace is learned via a regression algorithm with the l_2,1-norm used for feature selection. The maximum mean discrepancy (MMD) is then used to measure the feature divergence between different corpora. Tests show this algorithm gives satisfactory results for cross-corpus speech emotion recognition and is more robust and efficient than state-of-the-art transfer learning methods.

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feature selection / subspace learning / emotion recognition

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SONG Peng, ZHENG Wenming, ZHAO Li. Joint subspace learning and feature selection method for speech emotion recognition[J]. Journal of Tsinghua University(Science and Technology). 2018, 58(4): 347-351 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.014

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