  We present a deep-learning approach for the task of Concurrent Speaker
Detection (CSD) using a modified transformer model. Our model is designed to
handle multi-microphone data but can also work in the single-microphone case.
The method can classify audio segments into one of three classes: 1) no speech
activity (noise only), 2) only a single speaker is active, and 3) more than one
speaker is active. We incorporate a Cost-Sensitive (CS) loss and a confidence
calibration to the training procedure. The approach is evaluated using three
real-world databases: AMI, AliMeeting, and CHiME 5, demonstrating an
improvement over existing approaches.
