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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 |
| Editors | Hsin-Min Wang, Qingzhi Hou, Yuan Wei, Tan Lee, Jianguo Wei, Lei Xie, Hui Feng, Jianwu Dang, Jianwu Dang |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509042937 |
| DOIs | |
| Publication status | Published - 2017 May 2 |
| Event | 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 - Tianjin, China Duration: 2016 Oct 17 → 2016 Oct 20 |
Publication series
| Name | Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 |
|---|
Other
| Other | 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 |
|---|---|
| Country | China |
| City | Tianjin |
| Period | 16-10-17 → 16-10-20 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Linguistics and Language
Cite this
}
Detection of mood disorder using speech emotion profiles and LSTM. / Yang, Tsung Hsien; Wu, Chung-Hsien; Huang, Kun Yi; Su, Ming Hsiang.
Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016. ed. / Hsin-Min Wang; Qingzhi Hou; Yuan Wei; Tan Lee; Jianguo Wei; Lei Xie; Hui Feng; Jianwu Dang; Jianwu Dang. Institute of Electrical and Electronics Engineers Inc., 2017. 7918439 (Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Detection of mood disorder using speech emotion profiles and LSTM
AU - Yang, Tsung Hsien
AU - Wu, Chung-Hsien
AU - Huang, Kun Yi
AU - Su, Ming Hsiang
PY - 2017/5/2
Y1 - 2017/5/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85020206734&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020206734&partnerID=8YFLogxK
U2 - 10.1109/ISCSLP.2016.7918439
DO - 10.1109/ISCSLP.2016.7918439
M3 - Conference contribution
T3 - Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
BT - Proceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
A2 - Wang, Hsin-Min
A2 - Hou, Qingzhi
A2 - Wei, Yuan
A2 - Lee, Tan
A2 - Wei, Jianguo
A2 - Xie, Lei
A2 - Feng, Hui
A2 - Dang, Jianwu
A2 - Dang, Jianwu
PB - Institute of Electrical and Electronics Engineers Inc.
ER -