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A Method for Modelling and Simulation the Changes Trend of Emotions in Human Speech

Reza Ashrafidoost
Department of Computer Science IAU, Science and Research University, Iran

Saeed Setayeshi
Amirkabir University of Technology, Iran

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp17142479

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

Linköping Electronic Conference Proceedings 142:70, s. 479-486

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Publicerad: 2018-12-19

ISBN: 978-91-7685-399-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

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.

Nyckelord

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

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