Southeast University Multimodal Lie Detection Dataset

To address the lack of a Chinese context based lie detection dataset in current research, we have developed SEUMLD, which is the first publicly available multimodal lie detection dataset based on Chinese conversations. SEUMLD contains data in three modalities: video, audio, and electrocardiogram signals. In order to effectively stimulate the participants' motivation to lie, we designed a paradigm of simulated crime and simulated interrogation experiments. By recording multimodal signals of participants during simulated interrogation, SEUMLD collected data from 76 participants who had lived in a Chinese language environment for a long time, totaling 3224 conversations. This dataset provides coarse-grained annotation for identifying whether participants lie throughout the entire conversation, as well as fine-grained annotation for precise segmentation of each conversation.