  By using deep learning approaches, Speech Emotion Recog-nition (SER) on a
single domain has achieved many excellentresults. However, cross-domain SER is
still a challenging taskdue to the distribution shift between source and target
domains.In this work, we propose a Domain Adversarial Neural Net-work (DANN)
based approach to mitigate this distribution shiftproblem for cross-lingual
SER. Specifically, we add a languageclassifier and gradient reversal layer
after the feature extractor toforce the learned representation both
language-independent andemotion-meaningful. Our method is unsupervised, i. e.,
labelson target language are not required, which makes it easier to ap-ply our
method to other languages. Experimental results showthe proposed method
provides an average absolute improve-ment of 3.91% over the baseline system for
arousal and valenceclassification task. Furthermore, we find that batch
normaliza-tion is beneficial to the performance gain of DANN. Thereforewe also
explore the effect of different ways of data combinationfor batch
normalization.
