# Concurrent Speaker Detection: A multi-microphone Transformer-Based Approach

arXiv: https://arxiv.org/abs/2403.06856 · HTML: https://mlsanity.com/paper/2403.06856/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture](https://mlsanity.com/paper/2406.03272/) (2024)

## Abstract (arXiv)

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.