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...

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Main Authors:	Nwe, Tin Lay, Foo, Say Wei, De Silva, Liyanage C.
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
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
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-909222020-03-07T13:24:46Z Classification of stress in speech using linear and nonlinear features Nwe, Tin Lay Foo, Say Wei De Silva, Liyanage C. School of Electrical and Electronic Engineering IEEE International Conference on Acoustics, Speech and Signal Processing (2003 : Hong Kong) DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing 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. Accepted version 2009-07-31T06:50:13Z 2019-12-06T17:56:29Z 2009-07-31T06:50:13Z 2019-12-06T17:56:29Z 2003 2003 Conference Paper Dong, L., Foo, S. W., & Lian, Y. (2003). Modeling continuous visual speech using boosted viseme models. Proeedings of the 4th International Conference on Information, Communications and Signal Processing and the 4th IEEE Pacific-Rim Conference on Multimedia. (pp. 1394-1398). Singapore: IEEE. https://hdl.handle.net/10356/90922 http://hdl.handle.net/10220/5964 10.1109/ICASSP.2003.1202281 en IEEE International Conference on Acoustics, Speech, and Signal Processing © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site. 4 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Nwe, Tin Lay Foo, Say Wei De Silva, Liyanage C. Classification of stress in speech using linear and nonlinear features
description	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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Nwe, Tin Lay Foo, Say Wei De Silva, Liyanage C.
format	Conference or Workshop Item
author	Nwe, Tin Lay Foo, Say Wei De Silva, Liyanage C.
author_sort	Nwe, Tin Lay
title	Classification of stress in speech using linear and nonlinear features
title_short	Classification of stress in speech using linear and nonlinear features
title_full	Classification of stress in speech using linear and nonlinear features
title_fullStr	Classification of stress in speech using linear and nonlinear features
title_full_unstemmed	Classification of stress in speech using linear and nonlinear features
title_sort	classification of stress in speech using linear and nonlinear features
publishDate	2009
url	https://hdl.handle.net/10356/90922 http://hdl.handle.net/10220/5964
_version_	1681041445116444672

Classification of stress in speech using linear and nonlinear features

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