@inproceedings{bc87c67c5e4b4c6d98091f101625d60e,
title = "Emotional Climate Recognition in Interactive Conversational Speech Using Deep Learning",
abstract = "Emotions play a pivotal role in the individual's overall physical health. Therefore, there has been a steadily increasing interest towards emotion recognition in conversation (ERC). In this work, we propose bidirectional long short term memory (Bi-LSTM), convolutional neural network (CNN), and CNN-BiLSTM based models to predict the emotional climate established during the conversation by peers. Their speech signals across their conversation are analyzed using Mel frequency cepstral coefficients (MFCCs) that are then fed to the Bi-LSTM, CNN and CNN-BiLSTM models to predict the valence and arousal emotional climate cues. The proposed approach was tested on a publicly available dataset, namely K-EmoCon, that includes emotion labeling and peers' speech signals, during their conversation. The obtained results show that Bi-LSTM, CNN and CNN-BiLSTM models achieved a classification accuracy (arousal/valence) of 67.5%/57.7%, 73.3%/66.9%, and 75.1%/68.3%, respectively. These encouraging results show that a combination of deep learning schemes could increase the classification accuracy and provide efficient emotional climate recognition in naturalistic conversation environments.",
keywords = "Bi-LSTM, CNN, CNN-BiLSTM, conversational speech signals, deep learning, Emotion recognition in conversations, emotional climate, MFCC",
author = "Ghada Alhussein and Mohanad Alkhodari and Ahsan Khandokher and Hadjileontiadis, {Leontios J.}",
note = "Funding Information: This work is supported by Khalifa University of Science and Technology under Awards No. CIRA-2020-031. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Digital Health, ICDH 2022 ; Conference date: 10-07-2022 Through 16-07-2022",
year = "2022",
doi = "10.1109/ICDH55609.2022.00023",
language = "British English",
series = "Proceedings - 2022 IEEE International Conference on Digital Health, ICDH 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "96--103",
editor = "Ahamed, {Sheikh Iqbal} and Ardagna, {Claudio Augistino} and Hongyi Bian and Mario Bochicchio and Chang, {Carl K.} and Chang, {Rong N.} and Ernesto Damiani and Lin Liu and Misha Pavel and Corrado Priami and Hossain Shahriar and Robert Ward and Fatos Xhafa and Jia Zhang and Farhana Zulkernine",
booktitle = "Proceedings - 2022 IEEE International Conference on Digital Health, ICDH 2022",
address = "United States",
}