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Paper Information

Journal:   SIGNAL AND DATA PROCESSING   2008 , Volume - , Number 1 (SERIAL 9); Page(s) 85 To 96.
 
Paper: 

DETECTION OF STRESS IN SPEECH USING FEATURES DERIVED FROM NONLINEAR MODEL OF SPEECH PRODUCTION

 
 
Author(s):  TORABI S., ALMASGANJ F., MOHAMMADIAN A.
 
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Abstract: 

It is well known that speech signal is affected by speaker's psychological stress. Some of the recent works have evaluated different acoustic features individually, for detecting stress in speech and among these parameters, the nonlinear feature of TEO-CB-Auto-Env is known as the best one. In this work, a new mixed feature (TEO-Pch-LFPC) is proposed and investigated for the task of stress classification, using simulated domain of SUSAS database (styles of Neutral, Angry, Loud and Lombard). The precedence of this work is that it uses more simple classifiers rather than HMM (i.e. static classifiers of KNN, LDA and SVM), and the Round Robin Method is exerted. For pair-wise classification, the proposed approach reaches 93.78% and in multi-style case, the accuracy of 70.22% is obtained.

 
Keyword(s): DETECTION OF STRESS, DETECTION OF EMOTION, SPEECH PROCESSING
 
 
References: 
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Citations: 
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APA: Copy

TORABI, S., & ALMASGANJ, F., & MOHAMMADIAN, A. (2008). DETECTION OF STRESS IN SPEECH USING FEATURES DERIVED FROM NONLINEAR MODEL OF SPEECH PRODUCTION. SIGNAL AND DATA PROCESSING, -(1 (SERIAL 9)), 85-96. https://www.sid.ir/en/journal/ViewPaper.aspx?id=196868



Vancouver: Copy

TORABI S., ALMASGANJ F., MOHAMMADIAN A.. DETECTION OF STRESS IN SPEECH USING FEATURES DERIVED FROM NONLINEAR MODEL OF SPEECH PRODUCTION. SIGNAL AND DATA PROCESSING. 2008 [cited 2021October25];-(1 (SERIAL 9)):85-96. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=196868



IEEE: Copy

TORABI, S., ALMASGANJ, F., MOHAMMADIAN, A., 2008. DETECTION OF STRESS IN SPEECH USING FEATURES DERIVED FROM NONLINEAR MODEL OF SPEECH PRODUCTION. SIGNAL AND DATA PROCESSING, [online] -(1 (SERIAL 9)), pp.85-96. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=196868.



 
 
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