  Research on multilingual speech emotion recognition faces the problem that
most available speech corpora differ from each other in important ways, such as
annotation methods or interaction scenarios. These inconsistencies complicate
building a multilingual system. We present results for cross-lingual and
multilingual emotion recognition on English and French speech data with similar
characteristics in terms of interaction (human-human conversations). Further,
we explore the possibility of fine-tuning a pre-trained cross-lingual model
with only a small number of samples from the target language, which is of great
interest for low-resource languages. To gain more insights in what is learned
by the deployed convolutional neural network, we perform an analysis on the
attention mechanism inside the network.
