A cascade of TDOA-based spatial segmentation and speaker-embedding clustering diarizes meetings without multi-channel training data and handles overlapping speech and speaker position changes.
Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
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abstract
We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed system requires neither access to multi-channel training data nor prior knowledge about the number or placement of microphones. It works for both a compact microphone array and distributed microphones, with minor adjustments. Due to its superior handling of overlapping speech during segmentation, the proposed pipeline significantly outperforms the single-channel pyannote approach, both in a scenario with a compact microphone array and in a setup with distributed microphones. Additionally, we show that, unlike fully spatial diarization pipelines, the proposed system can correctly track speakers when they change positions.
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Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
A cascade of TDOA-based spatial segmentation and speaker-embedding clustering diarizes meetings without multi-channel training data and handles overlapping speech and speaker position changes.