Interruption Detection

Interruption detection research focuses on automatically identifying instances where one speaker begins talking before another finishes, classifying interruptions as cooperative or competitive, and detecting abnormal events in various communication settings. Current approaches leverage deep learning models, including variations of recurrent neural networks (RNNs) like LSTMs and GRUs, convolutional neural networks (CNNs), and transformer-based architectures, often trained on large datasets of transcribed dialogues or video conference recordings. This field is significant for improving the quality and efficiency of communication technologies, enhancing security in applications like video conferencing and satellite networks, and providing insights into human interaction dynamics.