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dc.contributor.authorO'Dwyer, Jonny
dc.contributor.authorMurray, Niall
dc.contributor.authorFlynn, Ronan
dc.date.accessioned2019-05-09T09:01:34Z
dc.date.available2019-05-09T09:01:34Z
dc.date.copyright2018-02
dc.date.issued2018
dc.identifier.citationO'Dwyer, J., Murray, N., Flynn, R. (2018). Affective computing using speech and eye gaze: a review and bimodal system proposal for continuous affect prediction. In - arXiv preprint arXiv:1805.06652, 2018.en_US
dc.identifier.otherSoftware Research Institute - Articlesen_US
dc.identifier.urihttps://research.thea.ie/handle/20.500.12065/2680
dc.description.abstractSpeech has been a widely used modality in the field of affective computing. Recently however, there has been a growing interest in the use of multi-modal affective computing systems. These multi-modal systems incorporate both verbal and non-verbal features for affective computing tasks. Such multi-modal affective computing systems are advantageous for emotion assessment of individuals in audio-video communication environments such as teleconferencing, healthcare, and education. From a review of the literature, the use of eye gaze features extracted from video is a modality that has remained largely unexploited for continuous affect prediction. This work presents a review of the literature within the emotion classification and continuous affect prediction sub-fields of affective computing for both speech and eye gaze modalities. Additionally, continuous affect prediction experiments using speech and eye gaze modalities are presented. A baseline system is proposed using open source software, the performance of which is assessed on a publicly available audio-visual corpus. Further system performance is assessed in a cross-corpus and cross-lingual experiment. The experimental results suggest that eye gaze is an effective supportive modality for speech when used in a bimodal continuous affect prediction system. The addition of eye gaze to speech in a simple feature fusion framework yields a prediction improvement of 6.13% for valence and 1.62% for arousal.en_US
dc.formatPDFen_US
dc.language.isoenen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/ie/*
dc.subjectHuman-computer interactionen_US
dc.subjectArtificial intelligenceen_US
dc.subjectUser interfaces (Computer systems)en_US
dc.titleAffective computing using speech and eye gaze: a review and bimodal system proposal for continuous affect prediction.en_US
dc.typeArticleen_US
dc.description.peerreviewyesen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-5919-0596
dc.rights.accessOpen Accessen_US
dc.subject.departmentSoftware Research Institute AITen_US


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Attribution-NonCommercial-NoDerivs 3.0 Ireland
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland