@inproceedings{fceebc2c94f247d59db8460c9dfa7a31,
title = "Hierarchical modeling of temporal course in emotional expression for speech emotion recognition",
abstract = "This paper presents an approach to hierarchical modeling of temporal course in emotional expression for speech emotion recognition. In the proposed approach, a segmentation algorithm is employed to hierarchically chunk an input utterance into three-level temporal units, including low-level descriptors (LLDs)-based sub-utterance level, emotion profile (EP)-based sub-utterance level and utterance level. An emotion-oriented hierarchical structure is constructed based on the three-level units to describe the temporal emotion expression in an utterance. A hierarchical correlation model is also proposed to fuse the three-level outputs from the corresponding emotion recognizers and further model the correlation among them to determine the emotional state of the utterance. The EMO-DB corpus was used to evaluate the performance on speech emotion recognition. Experimental results show that the proposed method considering the temporal course in emotional expression provides the potential to improve the speech emotion recognition performance.",
author = "Wu, {Chung Hsien} and Liang, {Wei Bin} and Cheng, {Kuan Chun} and Lin, {Jen Chun}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015 ; Conference date: 21-09-2015 Through 24-09-2015",
year = "2015",
month = dec,
day = "2",
doi = "10.1109/ACII.2015.7344666",
language = "English",
series = "2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "810--814",
booktitle = "2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015",
address = "United States",
}