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Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process(More)
In this paper, we present a comparative analysisof three classifiers for speech signal emotion recognition.Recognition was performed on emotional Berlin Database.This work focuses on speaker and utterance (phrase)dependent and independent framework. One hundred thirtythree (133) sound/speech features were extracted from Pitch,Mel Frequency Cepstral(More)
This paper presents an emotion recognition framework based on sound processing could significantly improve human computer interaction. One hundred thirty three (133) speech features obtained from sound processing of acting speech were tested in order to create a feature set sufficient to discriminate between seven emotions. Following statistical analysis in(More)
In the present paper a comparison of two classifiers for speech signal emotion recognition is presented. Recognition was performed on emotional Berlin Database. Within this work we concentrate on the evaluation of a speaker-dependent and speaker independent emotion recognition classification. One hundred thirty three (133) speech features obtained from(More)
Most of the pupils are witness bullying behavior at school. The variety of the roles that bystanders play in school bullying should be seen as a chance of promoting anti-bullying strategies and interventions. The authors suggest the usage of an automated speech emotion recognition technique as part of a multiple methods tool-box of exploring bullying, so(More)