Joint subspace learning and feature selection method for speech emotion recognition

doi:10.16511/j.cnki.qhdxxb.2018.26.014

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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.

Keywords feature selection subspace learning emotion recognition

ZTFLH:

TN912.3

Issue Date: 15 April 2018

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	SONG Peng
	ZHENG Wenming
	ZHAO Li

Cite this article:

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.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.26.014 OR http://jst.tsinghuajournals.com/EN/Y2018/V58/I4/347

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