The CG-MER dyadicmultimodal dataset for spontaneous french conversations: annotation, analysis and assessment benchmark

Emotion recognition is crucial for enhancing human-computer interaction systems. However, the development of robust
methodologies for French emotion recognition is hindered by the scarcity of labeled, interactive multimodal datasets. In this
work, we outline the acquisition and annotation procedures and provide an evaluation benchmark for the Card Game-based
Multimodal EmotionRecognition (CG-MER)dataset thatwedesigned to capture spontaneous emotional expressions in French
conversations. The dataset comprises approximately ten hours of video recordings featuring dyadic interactions between 20
French participants (11 males, 9 females) engaged in a card game, capturing natural expressions through facial cues, speech,
and gestures. Unlike existing corpora, CG-MER provides refined annotations across all three modalities, enabling a detailed
investigation of emotion dynamics and their associated gestures in a French-speaking context. Additionally, we establish
baseline results using state-of-the-art models for each modality and propose a standardized evaluation protocol, facilitating
future comparative studies on multimodal emotion recognition.