Cross-Corpus Speech Emotion Recognition Using Joint Distribution Adaptive Regression

Cross-Corpus Speech Emotion Recognition Using Joint Distribution Adaptive Regression

연구 분야: Artificial Intelligence

학회: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

In this paper, we focus on the research of cross-corpus speech emotion recognition (SER), in which the training and testing speech signals in cross-corpus SER belong to dierent speech corpus. Due to this fact, mismatched feature distributions may exist between the training and testing speech feature sets degrading the performance of most originally well-performing SER methods. To deal with cross-corpus SER, we propose a novel domain adaptation (DA) method called joint distribution adaptive regression (JDAR). The basic idea of JDAR is to learn a regression matrix by jointly considering the marginal and conditional probability distribution between the training and testing speech signals and hence their feature distribution dierence can be alleviated in the subspace spanned by the learned regression matrix. To evaluate the proposed JDAR, we conduct extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA speech databases. Experimental results show that the proposed JDAR achieves satisfactory performance and outperforms most of state-of-the-art subspace learning based DA methods.