Semi-supervised cross-lingual speech emotion recognition

Speech emotion recognition (SER) on a single language has achieved remarkable results through deep learning approaches over the last decade. However, cross-lingual SER remains a challenge in real-world applications due to (i) a large difference between the source and target domain distributions, (ii) the availability of few labeled and many unlabeled utterances for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when a few labels from the new language are available. Based on a Convolutional Neural Network (CNN), our method adapts to a new language by exploiting a pseudo-labeling strategy for the unlabeled utterances. In particular, the use of a hard and soft pseudo-labels approach is investigated. We thoroughly evaluate the performance of the method in a speaker-independent setup on both the source and the new language and show its robustness across five languages belonging to different linguistic strains.