Abstract
In this paper, we propose a deep learning-based psychological stress detection algorithm using speech signals. With increasing demands for communication between human and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human-machine interactions. The proposed algorithm first extracts mel-filterbank coefficients using preprocessed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using long short-term memory (LSTM) and feed-forward networks. To evaluate the performance of the proposed algorithm, speech, video, and bio-signal data were collected in a well-controlled environment. We utilized only speech signals in the decision process from subjects whose salivary cortisol level varies over 10%. Using the proposed algorithm, we achieved 66.4% accuracy in detecting the stress state from 25 subjects, thereby demonstrating the possibility of utilizing speech signals for automatic stress detection.
| Original language | English |
|---|---|
| Title of host publication | AVSU 2018 - Proceedings of the 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, Co-located with MM 2018 |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 11-15 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781450359771 |
| DOIs | |
| Publication status | Published - 2018 Oct 26 |
| Event | 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, AVSU 2018, co-located with MM 2018 - Seoul, Korea, Republic of Duration: 2018 Oct 26 → … |
Publication series
| Name | AVSU 2018 - Proceedings of the 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, Co-located with MM 2018 |
|---|
Conference
| Conference | 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, AVSU 2018, co-located with MM 2018 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 18/10/26 → … |
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All Science Journal Classification (ASJC) codes
- Software
- Media Technology
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
Cite this
}
A deep learning-based stress detection algorithm with speech signal. / Han, Hyewon; Byun, Kyunggeun; Kang, Hong-Goo.
AVSU 2018 - Proceedings of the 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, Co-located with MM 2018. Association for Computing Machinery, Inc, 2018. p. 11-15 (AVSU 2018 - Proceedings of the 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, Co-located with MM 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A deep learning-based stress detection algorithm with speech signal
AU - Han, Hyewon
AU - Byun, Kyunggeun
AU - Kang, Hong-Goo
PY - 2018/10/26
Y1 - 2018/10/26
N2 - In this paper, we propose a deep learning-based psychological stress detection algorithm using speech signals. With increasing demands for communication between human and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human-machine interactions. The proposed algorithm first extracts mel-filterbank coefficients using preprocessed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using long short-term memory (LSTM) and feed-forward networks. To evaluate the performance of the proposed algorithm, speech, video, and bio-signal data were collected in a well-controlled environment. We utilized only speech signals in the decision process from subjects whose salivary cortisol level varies over 10%. Using the proposed algorithm, we achieved 66.4% accuracy in detecting the stress state from 25 subjects, thereby demonstrating the possibility of utilizing speech signals for automatic stress detection.
AB - In this paper, we propose a deep learning-based psychological stress detection algorithm using speech signals. With increasing demands for communication between human and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human-machine interactions. The proposed algorithm first extracts mel-filterbank coefficients using preprocessed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using long short-term memory (LSTM) and feed-forward networks. To evaluate the performance of the proposed algorithm, speech, video, and bio-signal data were collected in a well-controlled environment. We utilized only speech signals in the decision process from subjects whose salivary cortisol level varies over 10%. Using the proposed algorithm, we achieved 66.4% accuracy in detecting the stress state from 25 subjects, thereby demonstrating the possibility of utilizing speech signals for automatic stress detection.
UR - http://www.scopus.com/inward/record.url?scp=85061791967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061791967&partnerID=8YFLogxK
U2 - 10.1145/3264869.3264875
DO - 10.1145/3264869.3264875
M3 - Conference contribution
AN - SCOPUS:85061791967
T3 - AVSU 2018 - Proceedings of the 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, Co-located with MM 2018
SP - 11
EP - 15
BT - AVSU 2018 - Proceedings of the 2018 Workshop on Audio-Visual Scene Understanding for Immersive Multimedia, Co-located with MM 2018
PB - Association for Computing Machinery, Inc
ER -