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arXiv · 2403.06856

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

Amit Eliav·Sharon Gannot
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Abstract

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.

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Amit Eliav, Sharon Gannot. 2024-03-11. Concurrent Speaker Detection: A multi-microphone Transformer-Based Approach. https://arxiv.org/abs/2403.06856

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