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Research on multilingual speech emotion recognition faces the problem thatmost available speech corpora differ from each other in important ways, such asannotation methods or interaction scenarios. These inconsistencies complicatebuilding a multilingual system. We present results for cross-lingual andmultilingual emotion recognition on English and French speech data with similarcharacteristics in terms of interaction (human-human conversations). Further,we explore the possibility of fine-tuning a pre-trained cross-lingual modelwith only a small number of samples from the target language, which is of greatinterest for low-resource languages. To gain more insights in what is learnedby the deployed convolutional neural network, we perform an analysis on theattention mechanism inside the network.

Cross-lingual and Multilingual Speech Emotion Recognition on English and French