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Appears in Collections:Computing Science and Mathematics Book Chapters and Sections
Peer Review Status: Refereed
Title: Emotional vocal expressions recognition using the COST 2102 Italian database of emotional speech
Author(s): Atassi, Hicham
Riviello, Maria Teresa
Smekal, Zdenek
Hussain, Amir
Esposito, Anna
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Editor(s): Esposito, A
Campbell, N
Vogel, C
Hussain, A
Nijholt, A
Citation: Atassi H, Riviello MT, Smekal Z, Hussain A & Esposito A (2010) Emotional vocal expressions recognition using the COST 2102 Italian database of emotional speech. In: Esposito A, Campbell N, Vogel C, Hussain A & Nijholt A (eds.) Development of Multimodal Interfaces: Active Listening and Synchrony: Second COST 2102 International Training School, Dublin, Ireland, March 23-27, 2009, Revised Selected Papers. Lecture Notes in Computer Science, 5967. Berlin Heidelberg: Springer, pp. 255-267.
Keywords: Emotion recognition
Italian database
spectral features
high level features
Issue Date: 2010
Date Deposited: 12-Aug-2013
Series/Report no.: Lecture Notes in Computer Science, 5967
Abstract: The present paper proposes a new speaker-independent approach to the classification of emotional vocal expressions by using the COST 2102 Italian database of emotional speech. The audio records extracted from video clips of Italian movies possess a certain degree of spontaneity and are either noisy or slightly degraded by an interruption making the collected stimuli more realistic in comparison with available emotional databases containing utterances recorded under studio conditions. The audio stimuli represent 6 basic emotional states: happiness, sarcasm/irony, fear, anger, surprise, and sadness. For these more realistic conditions, and using a speaker independent approach, the proposed system is able to classify the emotions under examination with 60.7% accuracy by using a hierarchical structure consisting of a Perceptron and fifteen Gaussian Mixture Models (GMM) trained to distinguish within each pair (couple) of emotions under examination. The best features in terms of high discriminative power were selected by using the Sequential Floating Forward Selection (SFFS) algorithm among a large number of spectral, prosodic and voice quality features. The results were compared with the subjective evaluation of the stimuli provided by human subjects.
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