@inproceedings{f7f9fe3beed04e57a44f29104af47c88,
title = "Emotion recognition of conversational affective speech using temporal course modeling-based error weighted cross-correlation model",
abstract = "A complete emotional expression in natural face-to-face conversation typically contains a complex temporal course. In this paper, we propose a temporal course modeling-based error weighted cross-correlation model (TCM-EWCCM) for speech emotion recognition. In TCM-EWCCM, a TCM-based cross-correlation model (CCM) is first used to not only model the temporal evolution of the extracted acoustic and prosodie features individually but also construct the statistical dependencies among paired acoustic-prosodic features in different emotional states. Then, a Bayesian classifier weighting scheme named error weighted classifier combination is adopted to explore the contributions of the individual TCM-based CCM classifiers for different acoustic-prosodic feature pairs to enhance the speech emotion recognition accuracy. The results of experiments on the NCKU-CASC corpus demonstrate that modeling the complex temporal structure and considering the statistical dependencies as well as contributions among paired features in natural conversation speech can indeed improve the speech emotion recognition performance.",
author = "Lin, {Jen Chun} and Wei, {Wen Li} and Wu, {Chung Hsien} and Wang, {Hsin Min}",
year = "2014",
month = feb,
day = "12",
doi = "10.1109/APSIPA.2014.7041621",
language = "English",
series = "2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014",
address = "United States",
note = "2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014 ; Conference date: 09-12-2014 Through 12-12-2014",
}