Cross-corpus speech emotion recognition based on a feature transfer learning method

doi:10.16511/j.cnki.qhdxxb.2016.26.008

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Abstract Speech emotion recognition systems offen use training data and testing data from different corpora, so the recognition rates decrease drastically. This paper presents a feature transfer learning method for cross-corpora speech emotion recognition. The maximum mean discrepancy (MMD) is used to describe the similarities between the emotional feature distributions of the different corpora, then the latent close low dimensional feature space is obtained via the maximum mean discrepancy embedding (MMDE) and dimension reduction algorithms, with the classifiers then trained in this space for emotion recognition. A semi-supervised discriminative analysis (SDA) algorithm is further used for dimension reduction to better ensure the class discrimination of the emotional features. Tests on two popular speech emotion datasets demonstrate that this method efficiently improves the recognition rates for cross-corpora speech emotion recognition.

Keywords speech emotion recognition transfer learning feature dimension reduction semi-supervised discriminative analysis

ZTFLH:

TN912.3

Issue Date: 15 November 2016

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	SONG Peng
	ZHENG Wenming
	ZHAO Li

Cite this article:

SONG Peng,ZHENG Wenming,ZHAO Li. Cross-corpus speech emotion recognition based on a feature transfer learning method[J]. Journal of Tsinghua University(Science and Technology), 2016, 56(11): 1179-1183.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2016.26.008 OR http://jst.tsinghuajournals.com/EN/Y2016/V56/I11/1179

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