A Multimodal Approach to Confidence, Emotion, and Personality Assessment in Virtual Interviews
DOI:
https://doi.org/10.5269/bspm.82391Resumen
Therecruitmentlandscapeemphasizesprioritizingcandidatestechnicalskillsoverinterpersonal
skills suchasemotional intelligence, suitablebehaviour, communicationstyleandadaptability. Traditional
systemsanalysetextorskillbasedresponses,therebymissingouttoanalysebehaviourandrealtimeemotions
ininterview. Thiscreatesgapinanalysingthecandidatestruepersonalityprofileandstyleof interaction.
Therefore, there isnecessityofmultimodal systemtoevaluate ,whatcandidatespeak,howtheyspeakand
howtheypresentthemselvesduringinterview.Toaddressthis limitations, thisstudyproposesamultimodal
emotionrecognitionsystemwhichstrengthensforpersonalityassessmentindigitalinterviews.Thisframework
processesbyintegratinglinguisticcues fromtextual transcripts,paralinguisticcues fromspeechsignalsand
visualexpressionsfromvideoframes.Textbasedemotionfeaturesareextractedusingafinetunedtransformer
model,achieved43%accuracy. Audiobasedemotional cuesarerecognizedusingaspeechemotionpipeline,
achieved63%accuracy.Facialexpressionsareanalysedusingvisionbasedmodule.Earlyfusionstrategyhas
beenappliedto integratethesemultiplemodalitieswhichenables thesystemtogetconsolidatedemotional
statefromtheinterview.Thismultimodalarchitecturegivesagreatsupporttoevaluatecandidatecomplete
profile,whichmakes therecruitmentdecisionholistic. Thismakes theorganizations tohirethecandidates
whoarenotonlytechnicallystrongbutalsowithgoodinterpersonalskills.
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Derechos de autor 2026 Boletim da Sociedade Paranaense de Matemática
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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