Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/74844
Title: A text-independent, multi-lingual and cross-corpus evaluation of emotion recognition in speech
Authors: Sammut, Alessandro (2019)
Keywords: Human-computer interaction
Speech processing systems
Emotions -- Computer simulation
Issue Date: 2019
Citation: Sammut, A. (2019). A text-independent, multi-lingual and cross-corpus evaluation of emotion recognition in speech (Bachelor's dissertation).
Abstract: Ongoing research on Human Computer Interaction (HCI) is always progressing and the need for machines to detect human emotion continues to increase for the purposes of having more personalized systems which can intelligently act according to user emotion. Varying languages may portray emotions di↵erently which is a hiccup in the field of automatic emotion recognition from speech. We propose a system which takes a cross-corpus and multilingual approach to emotion recognition from speech in order to show the behaviour of results when compared to single monolingual corpus testing. We utilize four di↵erent classifiers: K-Nearest Neighbours (KNN), Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), Gaussian Mixture Models (GMM) along with two di↵erent feature sets including Mel-Frequency Cepstral Coefficients (MFCCs) and our own extracted prosodic feature set on three di↵erent emotional speech corpora containing of several languages. The aim for the prosodic feature set is to try and acquire a general feature set that works well across all languages and corpora. We notice a drop in performance when unseen data is tested but made better when merged databases are present in the training data and when EMOVO is present in either training or testing. MFCCs work very well with GMMs on single corpus testing but our prosodic feature set works better in general on the rest of the classifiers. We evaluate all the obtained results in view of proving any elements that could possibly form a language independent system but for the time being results show otherwise.
Description: B.SC.ICT(HONS)ARTIFICIAL INTELLIGENCE
URI: https://www.um.edu.mt/library/oar/handle/123456789/74844
Appears in Collections:Dissertations - FacICT - 2019
Dissertations - FacICTAI - 2019

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