  Concurrent Speaker Detection (CSD), the task of identifying the presence and
overlap of active speakers in an audio signal, is crucial for many audio tasks
such as meeting transcription, speaker diarization, and speech separation. This
study introduces a multimodal deep learning approach that leverages both audio
and visual information. The proposed model employs an early fusion strategy
combining audio and visual features through cross-modal attention mechanisms,
with a learnable [CLS] token capturing the relevant audio-visual relationships.
  The model is extensively evaluated on two real-world datasets, AMI and the
recently introduced EasyCom dataset. Experiments validate the effectiveness of
the multimodal fusion strategy. Ablation studies further support the design
choices and the training procedure of the model. As this is the first work
reporting CSD results on the challenging EasyCom dataset, the findings
demonstrate the potential of the proposed multimodal approach for CSD in
real-world scenarios.
