Classification of stress in speech using linear and nonlinear features

Classification of stress in speech using linear and nonlinear features

In this paper, three systems for classification of stress in speech are proposed. The first system makes use of linear short time Log Frequency Power Coefficients (LFPC), the second employs Teager Energy Operator (TEO) based Nonlinear Frequency Domain LFPC features (NF...

Full description

Bibliographic Details
Main Authors: Nwe, Tin Lay, Foo, Say Wei, De Silva, Liyanage C.
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2009
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:https://hdl.handle.net/10356/90922
http://hdl.handle.net/10220/5964
Description
Summary:In this paper, three systems for classification of stress in speech are proposed. The first system makes use of linear short time Log Frequency Power Coefficients (LFPC), the second employs 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 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, the average accuracy of 84% and the best accuracy.

Similar Items