Nonnegative Matrix Factorization Based Transfer Subspace Learning for Cross-Corpus Speech Emotion Recognition

Abstract

This article focuses on the cross-corpus speech emotion recognition (SER) task. To overcome the problem that the distribution of training (source) samples is inconsistent with that of testing (target) samples, we propose a non-negative matrix factorization based transfer subspace learning method (NMFTSL). Our method tries to find a shared feature subspace for the source and target corpora, in which the discrepancy between the two distributions is eliminated as much as possible and their individual components are excluded, thus the knowledge of the source corpus can be transferred to the target corpus. Specifically, in this induced subspace, we minimize the distances not only between the marginal distributions but also between the conditional distributions, where both distances are measured by the maximum mean discrepancy criterion. To estimate the conditional distribution of the target corpus, we propose to integrate the prediction of target label and the learning of feature representation into a joint learning model. Meanwhile, we introduce a difference loss to exclude the individual components from the shared subspace, which can further reduce the mutual interference between the source and target individual components. Moreover, we propose a discrimination loss to introduce the labels into the shared subspace, which can improve the discrimination ability of the feature representation. We also provide the solution for the corresponding optimization problem. To evaluate the performance of our method, we construct 30 cross-corpus SER schemes using 6 popular speech emotion corpora. Experimental results show that our approach achieves better overall performance than state-of-the-art methods.