Abstract
In this paper we introduce a novel approach that robust automatic speech features recognition of one's emotion is achieved in a classification model named decision forest. The 13th order of Mel-frequency ceptstrum coefficients (MFCC) vector is processed as the multivariate data that will be imported to our classifier. In order to draw underlying and inductive information behind the MFCC feature, our decision forest classifier contains two stages to make classification, a supervised clustering based pattern extraction stage and a soft discretization based decision forest stage. Finally, a Japanese emotion corpus used for training and evaluation is described in detail. The results in recognition of six discrete emotions exceeded a mean value of 81% recognition rate.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Communications |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781479923854 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | 2014 10th International Conference on Communications, COMM 2014 - Bucharest Duration: 2014 May 29 → 2014 May 31 |
Other
| Other | 2014 10th International Conference on Communications, COMM 2014 |
|---|---|
| City | Bucharest |
| Period | 14/5/29 → 14/5/31 |
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Keywords
- classification
- consensus building
- decision forest
- MFCC
- speech emotion recognition
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Networks and Communications
Cite this
Emotional speech classification in consensus building. / He, Ning; Yao, Shuoqing; Yoshie, Osamu.
IEEE International Conference on Communications. Institute of Electrical and Electronics Engineers Inc., 2014. 6866670.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Emotional speech classification in consensus building
AU - He, Ning
AU - Yao, Shuoqing
AU - Yoshie, Osamu
PY - 2014
Y1 - 2014
N2 - In this paper we introduce a novel approach that robust automatic speech features recognition of one's emotion is achieved in a classification model named decision forest. The 13th order of Mel-frequency ceptstrum coefficients (MFCC) vector is processed as the multivariate data that will be imported to our classifier. In order to draw underlying and inductive information behind the MFCC feature, our decision forest classifier contains two stages to make classification, a supervised clustering based pattern extraction stage and a soft discretization based decision forest stage. Finally, a Japanese emotion corpus used for training and evaluation is described in detail. The results in recognition of six discrete emotions exceeded a mean value of 81% recognition rate.
AB - In this paper we introduce a novel approach that robust automatic speech features recognition of one's emotion is achieved in a classification model named decision forest. The 13th order of Mel-frequency ceptstrum coefficients (MFCC) vector is processed as the multivariate data that will be imported to our classifier. In order to draw underlying and inductive information behind the MFCC feature, our decision forest classifier contains two stages to make classification, a supervised clustering based pattern extraction stage and a soft discretization based decision forest stage. Finally, a Japanese emotion corpus used for training and evaluation is described in detail. The results in recognition of six discrete emotions exceeded a mean value of 81% recognition rate.
KW - classification
KW - consensus building
KW - decision forest
KW - MFCC
KW - speech emotion recognition
UR - http://www.scopus.com/inward/record.url?scp=84907379528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907379528&partnerID=8YFLogxK
U2 - 10.1109/ICComm.2014.6866670
DO - 10.1109/ICComm.2014.6866670
M3 - Conference contribution
AN - SCOPUS:84907379528
SN - 9781479923854
BT - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
ER -