Comparison Analysis of Classifiers for Speech under Stress
Xiao Lan Yao, Ning Xu, Mingsheng Gao, Aiming Jiang, Xiaofeng Liu · 2016
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.