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This paper focused on improving Big Five personality trait estimation in group discussions via multimodal analysis and transfer learning with the state-of-the-art speaker individuality feature, namely, the identity vector (i-vector) speaker embedding. The experiments were carried out by investigating the effective and robust multimodal features for estimation with two group discussion datasets, i.e., the Multimodal Task-Oriented Group Discussion (MATRICS) (in Japanese) and Emergent Leadership (ELEA) (in European languages) corpora. Subsequently, the evaluation was conducted by using leave-one-person-out cross-validation (LOPCV) and ablation tests to compare the effectiveness of each modality. The overall results showed that the speaker-dependent features, e.g., the i-vector, effectively improved the prediction accuracy of Big Five personality trait estimation. 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