@inproceedings{e443810d63fd43da923aa64690d7b7e4,
title = "Ensembling Multilingual Pre-Trained Models for Predicting Multi-Label Regression Emotion Share from Speech",
abstract = "Speech emotion recognition has evolved from research to practical applications. Previous studies of emotion recognition from speech have focused on developing models on certain datasets like IEMOCAP. The lack of data in the domain of emotion modeling emerges as a challenge to evaluate models in the other dataset, as well as to evaluate speech emotion recognition models that work in a multilingual setting. This paper proposes an ensemble learning to fuse results of pre-trained models for emotion share recognition from speech. The models were chosen to accommodate multilingual data from English and Spanish. The results show that ensemble learning can improve the performance of the baseline model with a single model and the previous best model from the late fusion. The performance is measured using the Spearman rank correlation coefficient since the task is a regression problem with ranking values. A Spearman 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 than the previous study of a fusion method from monolingual data, which achieved scores of 0.476 for the test and 0.470 for the development.",
keywords = "emotion share, ensemble learning, multilingual speech processing, pre-trained model, speech emotion recognition",
author = "Atmaja, {Bagus Tris} and Akira Sasou",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 ; Conference date: 31-10-2023 Through 03-11-2023",
year = "2023",
doi = "10.1109/APSIPAASC58517.2023.10317109",
language = "English",
series = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1026--1029",
booktitle = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",
address = "United States",
}