Speech emotion recognition with ensemble learning methods
Po-Yuan Shih, Chia-Ping Chen, Chung-Hsien Wu
Abstract
In this paper, we propose to apply ensemble learning methods on neural networks to improve the performance of speech emotion recognition tasks. The basic idea is to first divide unbalanced data set into balanced subsets and then combine the predictions of the models trained on these subsets. Several methods regarding the decomposition of data and the exploitation of model predictions are investigated in this study. On the public-domain FAU-Aibo database, which is used in Interspeech Emotion Challenge evaluation, the best performance we achieve is an unweighted average (UA) recall rate of 45.5% for the 5-class classification task. Furthermore, such performance is achieved with a feature space of 40-dimension. Compared to the baseline system with 384-dimension feature vector per example and an UA of 38.9%, such a performance is very impressive. Indeed, this is one of the best performances on FAU-Aibo within the static modeling framework.
BibTeX
@inproceedings{icassp2017_speechemotionrec,
title = {Speech emotion recognition with ensemble learning methods},
author = {Po-Yuan Shih and Chia-Ping Chen and Chung-Hsien Wu},
booktitle = {ICASSP 2017},
year = {2017}
}