Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | A Method for Modelling and Simulation the Changes Trend of Emotions in Human Speech Linköping University Electronic Press Conference Proceedings
Göm menyn

Title:
A Method for Modelling and Simulation the Changes Trend of Emotions in Human Speech
Author:
Reza Ashrafidoost: Department of Computer Science IAU, Science and Research University, Iran Saeed Setayeshi: Amirkabir University of Technology, Iran
DOI:
10.3384/ecp17142479
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
070
Pages:
479-486
No. of pages:
8
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

One of the fastest and richest methods, which represents emotional profile of human beings is speech. It also conveys the mental and perceptual concepts between humans. In this paper we have addressed the recognition of emotional characteristics of speech signal and propose a method to model the emotional changes of the utterance during the speech by using a statistical learning method. In this procedure of speech recognition, the internal feelings of the individual speaker are processed, and then classified during the speech. And so on, the system classifies emotions of the utterance in six standard classes including, anger, boredom, fear, disgust, neutral and sadness. For that reason, we call the standard and widely used speech database, EmoDB for training phase of proposed system. When pre-processing tasks done, speech patterns and features are extracted by MFCC method, and then we apply a classification approach based on statistical learning classifier to simulate changes trend of emotional states. Empirical experimentation indicates that we have achieved 85.54% of average accuracy rate and the score 2.5 of standard deviation in emotion recognition.

Keywords: emotional speech modelling, speech recognition, human-computer interaction (HCI), gaussian mixture model (GMM), mel frequency cepstral co-efficient

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Reza Ashrafidoost, Saeed Setayeshi
Title:
A Method for Modelling and Simulation the Changes Trend of Emotions in Human Speech
DOI:
http://dx.doi.org/10.3384/ecp17142479
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Reza Ashrafidoost, Saeed Setayeshi
Title:
A Method for Modelling and Simulation the Changes Trend of Emotions in Human Speech
DOI:
https://doi.org10.3384/ecp17142479
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2019-10-02