  The majority of existing speech emotion recognition research focuses on
automatic emotion detection using training and testing data from same corpus
collected under the same conditions. The performance of such systems has been
shown to drop significantly in cross-corpus and cross-language scenarios. To
address the problem, this paper exploits a transfer learning technique to
improve the performance of speech emotion recognition systems that is novel in
cross-language and cross-corpus scenarios. Evaluations on five different
corpora in three different languages show that Deep Belief Networks (DBNs)
offer better accuracy than previous approaches on cross-corpus emotion
recognition, relative to a Sparse Autoencoder and SVM baseline system. Results
also suggest that using a large number of languages for training and using a
small fraction of the target data in training can significantly boost accuracy
compared with baseline also for the corpus with limited training examples.
