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Register a new userMUSER: MUltimodal Stress detection using Emotion Recognition as an Auxiliary Task
Muser: كشف الإجهاد المتعدد الوسائط باستخدام التعرف على العاطفة كامرأة مساعدة
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يمكن للقدرة على اكتشاف الإجهاد البشري تلقائيا أن تفيد العوامل الذكية الاصطناعية المشاركة في الحوسبة العاطفية والتفاعل البشري والحاسوب.الإجهاد والعاطفة كلا من الدول العاطفية البشرية، وقد أثبت الإجهاد أن يكون لها آثار مهمة على تنظيم العاطفة والتعبير عنها.على الرغم من أن سلسلة من الأساليب قد تم تأسيسها للكشف عن الإجهاد المتعدد الوسائط، فقد تم اتخاذ خطوات محدودة لاستكشاف الاعتماد الوارد في الاتجاهات الأساسية بين الإجهاد والعاطفة.في هذا العمل، نحقق في قيمة التعرف على العاطفة كملقمة مساعدة لتحسين اكتشاف الإجهاد.نقترح Muser - وهي عبارة عن بنية نموذجية قائمة على المحولات وخوارزمية تعليمية متعددة المهام الجديدة مع استراتيجية أخذ العينات الديناميكية المستندة إلى السرعة.يوضح التقييم في مجموعة بيانات المشاعر المشددة متعددة الوسائط (MUSE) أن طرازنا فعال للكشف عن الإجهاد بالمهام المساعدة الداخلية والخارجية، وتحقق نتائج أحدث النتائج.
The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important implications on the regulation and expression of emotion. Although a series of methods have been established for multimodal stress detection, limited steps have been taken to explore the underlying inter-dependence between stress and emotion. In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. We propose MUSER -- a transformer-based model architecture and a novel multi-task learning algorithm with speed-based dynamic sampling strategy. Evaluation on the Multimodal Stressed Emotion (MuSE) dataset shows that our model is effective for stress detection with both internal and external auxiliary tasks, and achieves state-of-the-art results.
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