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Recent advances in AI-based learning models have significantly increased the accuracy of Automatic Personality Recognition (APR). However, these methods either require training data from the same subject or the meta-information from the training set to learn the personality-related features (i.e., subject-dependency). The variance of feature extraction for different subjects compromises the possibility of designing a dependency-free system for APR. To address this problem, we present an unsupervised multimodal learning framework to infer personality traits from audio, visual, and verbal modalities. Our method both extracts the handcraft features and transfers deep-learning based embeddings from other tasks (e.g., emotion recognition) to recognize personality traits. Since these representations are extracted locally in the time domain, we present an unsupervised temporal aggregation method to aggregate the …