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+as+part+of+the+6th+Workshop+and+Competition+on+Affective+Behavior+Analysis+in-the-wild.+Our+methodology+utilises+the+Wav2Vec+2.0+architecture%252C+which+has+been+pre-trained+on+an+extensive+podcast+dataset%252C+to+capture+a+wide+array+of+audio+features+that+include+both+linguistic+and+paralinguistic+components.+We+refine+our+feature+extraction+process+by+employing+a+fusion+technique+that+combines+individual+features+with+a+global+mean+vector%252C+thereby+embedding+a+broader+contextual+understanding+into+our+analysis.+A+key+aspect+of+our+approach+is+the+multi-task+fusion+strategy+that+not+only+leverages+these+features+but+also+incorporates+a+pre-trained+Valence-Arousal-Dominance+(VAD)+model.+This+integration+is+designed+to+refine+emotion+intensity+prediction+by+concurrently+processing+multiple+emotional+dimensions%252C+thereby+embedding+a+richer+contextual+understanding+into+our+framework.+For+the+temporal+analysis+of+audio+data%252C+our+feature+fusion+process+utilises+a+Long+Short-Term+Memory+(LSTM)+network.+This+approach%252C+which+relies+solely+on+the+provided+audio+data%252C+shows+marked+advancements+over+the+existing+baseline%252C+offering+a+more+comprehensive+understanding+of+emotional+mimicry+in+naturalistic+settings%252C+achieving+the+second+place+in+the+EMI+challenge.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.aab964d7e805afb3c76c9473ec0cd60a%3frik%3djX3KjooGU%252fTYmg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Unimodal+PDF&FORM=IRPRST&ck=EE7A93F135542C6EDFC07BE928B4B8C5&selectedIndex=54&itb=0)
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