Joint subspace learning and feature selection method for speech emotion recognition

被引：0

作者：

Song P. ^{[1
]}

Zheng W. ^{[2
]}

Zhao L. ^{[2
]}

机构：

[1] School of Computer and Control Engineering, Yantai University, Yantai

[2] Key Laboratory of Child Development and Learning Science of Ministry of Education, Southeast University, Nanjing

来源：

Qinghua Daxue Xuebao/Journal of Tsinghua University | 2018年 / 58卷 / 04期

关键词：

Emotion recognition; Feature selection; Subspace learning;

D O I：

10.16511/j.cnki.qhdxxb.2018.26.014

中图分类号：

学科分类号：

摘要：

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 l2,1-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. © 2018, Tsinghua University Press. All right reserved.

引用

页码：347 / 351

页数：4

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