Publication Type : Conference Paper
Publisher : Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018
Source : Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018, Institute of Electrical and Electronics Engineers Inc., p.426-432 (2018)
ISBN : 9781538635216
Keywords : Accuracy Improvement, Attribute selection, Decision tree classifiers, Decision trees, Dimensionality reduction, INTERSPEECH 2010, MFCC, Optimization, Patient monitoring, Sequential minimal optimization, Signal processing, Speech recognition, Stress recognition
Campus : Bengaluru
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : pIn this study, a novel approach to detect five different stress conditions in a simulated stress database in Hindi language using Interspeech 2010 features are proposed which can be used in psychotherapy in an Indian context to monitor the stress conditions of patients. The features are extracted using an OpenSMILE toolkit. The Naive Bayes, Sequential Minimal Optimization SMO, Simple Logistic and decision tree classifier are considered for classification. The dimensionality reduction is implemented using an attribute selection technique which makes use of a search method. The accuracy rate achieved using each classifier are tabulated and compared. The random forest decision tree classifier is found to achieve better results. The proposed method achieved a 3 point accuracy improvement without using attribute selection and 1.3 point accuracy improvement with attribute selection when compared with the reported existing work done using the same database. © 2018 IEEE./p
Cite this Research Publication : V. Narayanan, Lalitha, S., and Gupta, D., “Stress Recognition Using Auditory Features for Psychotherapy in Indian Context”, in Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, ICCSP 2018, 2018, pp. 426-432.