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Detection of Mood Disorder Using Speech Emotion Profiles and LSTM

Abstract: 

In mood disorder diagnosis, bipolar disorder (BD) patients are often misdiagnosed as unipolar depression (UD) on initial presentation. It is crucial to establish an accurate distinction between BD and UD to make a correct and early diagnosis, leading to improvements in treatment and course of illness. To deal with this misdiagnosis problem, in this study, we experimented on eliciting subjects’ emotions by watching six eliciting emotional video clips. After watching each video clips, their speech responses were collected when they were interviewing with a clinician. In mood disorder detection, speech emotions play an import role to detect manic or depressive symptoms. Therefore, speech emotion profiles (EP) are obtained by using the support vector machine (SVM) which are built via speech features adapted from selected databases using a denoising autoencoder-based method. Finally, a Long Short-Term Memory (LSTM) recurrent neural network is employed to characterize the temporal information of the EPs with respect to six emotional videos. Comparative experiments clearly show the promising advantage and efficacy of the LSTM-based approach for mood disorder detection.

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Paper Details

Authors:
Tsung-Hsien Yang, Kun-Yi Huang, and Ming-Hsiang Su
Submitted On:
14 October 2016 - 9:30pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Chung-Hsien Wu
Paper Code:
114
Document Year:
2016
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Document Files

ISCSLP-2016-1014-1.pdf

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[1] Tsung-Hsien Yang, Kun-Yi Huang, and Ming-Hsiang Su, "Detection of Mood Disorder Using Speech Emotion Profiles and LSTM", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1183. Accessed: Feb. 27, 2020.
@article{1183-16,
url = {http://sigport.org/1183},
author = {Tsung-Hsien Yang; Kun-Yi Huang; and Ming-Hsiang Su },
publisher = {IEEE SigPort},
title = {Detection of Mood Disorder Using Speech Emotion Profiles and LSTM},
year = {2016} }
TY - EJOUR
T1 - Detection of Mood Disorder Using Speech Emotion Profiles and LSTM
AU - Tsung-Hsien Yang; Kun-Yi Huang; and Ming-Hsiang Su
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1183
ER -
Tsung-Hsien Yang, Kun-Yi Huang, and Ming-Hsiang Su. (2016). Detection of Mood Disorder Using Speech Emotion Profiles and LSTM. IEEE SigPort. http://sigport.org/1183
Tsung-Hsien Yang, Kun-Yi Huang, and Ming-Hsiang Su, 2016. Detection of Mood Disorder Using Speech Emotion Profiles and LSTM. Available at: http://sigport.org/1183.
Tsung-Hsien Yang, Kun-Yi Huang, and Ming-Hsiang Su. (2016). "Detection of Mood Disorder Using Speech Emotion Profiles and LSTM." Web.
1. Tsung-Hsien Yang, Kun-Yi Huang, and Ming-Hsiang Su. Detection of Mood Disorder Using Speech Emotion Profiles and LSTM [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1183