Cross-corpus speech emotion recognition can be a useful transfer learning technique to build a robust speech emotion recognition system by leveraging information from various speech datasets - cross-language and cross-corpus. However, more research needs to be carried out to understand the effective operating scenarios of cross-corpus speech emotion recognition, especially with the utilization of the powerful deep learning techniques. In this paper, we use five different corpora of three different languages to investigate the cross-corpus and cross-language emotion recognition using Deep Belief Networks (DBNs). Experimental results demonstrate that DBNs with generalization power offers better accuracy than a discriminative method based on Sparse Auto Encoder and SVM. Results also suggest that using a large number of languages for training and using a small fraction of target data in training can significantly boost accuracy compared to using the same language for training and testing.
View on arXiv