Description: Multimodal Synchronization Models are approaches that enable the integration and analysis of data from different modalities, such as text, audio, video, and sensors. These models are essential for creating coherent and meaningful analysis, as each modality provides unique information that, when combined, enriches the understanding of the studied phenomenon. Synchronization refers to the ability to temporally align data from different sources, which is crucial in contexts where the interaction between modalities is dynamic and complex. For example, in the analysis of human interactions, video data (visual observation), audio (dialogue), and text (transcriptions) can be synchronized to gain a more comprehensive view of communication. Multimodal Synchronization Models are used in various fields, including artificial intelligence, psychology, education, and healthcare, where the integration of multiple data sources can lead to deeper insights and improved decision-making processes.
