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Speech emotion recognition has evolved from research to practicalapplications. Previous studies of emotion recognition from speech have focusedon developing models on certain datasets like IEMOCAP. The lack of data in thedomain of emotion modeling emerges as a challenge to evaluate models in theother dataset, as well as to evaluate speech emotion recognition models thatwork in a multilingual setting. This paper proposes an ensemble learning tofuse results of pre-trained models for emotion share recognition from speech.The models were chosen to accommodate multilingual data from English andSpanish. The results show that ensemble learning can improve the performance ofthe baseline model with a single model and the previous best model from thelate fusion. The performance is measured using the Spearman rank correlationcoefficient since the task is a regression problem with ranking values. ASpearman rank correlation coefficient of 0.537 is reported for the test set,while for the development set, the score is 0.524. These scores are higher thanthe previous study of a fusion method from monolingual data, which achievedscores of 0.476 for the test and 0.470 for the development.

Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech