Comparison Analysis of Classifiers for Speech under Stress
- Resource Type
- Conference
- Authors
- Yao, Xiao; Xu, Ning; Gao, Mingsheng; Jiang, Aiming; Liu, Xiaofeng
- Source
- 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) ITHINGS-GREENCOM-CPSCOM-SMARTDATA Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE International Conference on. :429-432 Dec, 2016
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Speech
Hidden Markov models
Support vector machines
Stress
Kernel
Speech recognition
Training data
stress classification
physiological system
gaussian mixture model
support vector machines
- Language
In this paper, we focus on the classification of neutral and stressed speech. The parameters representing airflow patterns in physiological system are achieved using a physical model. Speech features were modeled using Gaussian Mixture Models (GMM) and Support Vector Machines (SVM). A comparison is made of different classifiers to determine their performance in stressed speech classification. Results show that SVM outperforms the standard GMM and linear classifiers, because SVM can better solve the small sample size problem, which often occurs in stressed speech classification tasks.