Classification of stress in speech using linear and nonlinear features

Abstract

Three systems for the classification of stress in speech are proposed. The first system makes use of linear short time log frequency power coefficients (LFPC), the second employs a Teager energy operator (TEO) based nonlinear frequency domain LFPC features (NFD-LFPC) and the third uses TEO based nonlinear time domain LFPC features (NTD-LFPC). The systems were tested using the SUSAS (speech under simulated and actual stress) database to categorize five stress conditions individually. Results show that the system using LFPC gives the highest accuracy, followed by the system using NFD-LFPC features, while the system using NTD-LFPC features gives the worst performance. For the system using linear LFPC features, average accuracy of 84% and best accuracy of 95% were obtained in classifying five stress categories.