融合声源定位与说话人嵌入，实现无需麦克风配置先验的会议语音分段聚类。
Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering
- 先用时差定位分割语音段，再用嵌入向量聚类区分说话人。
- 在紧凑阵列和分布式麦克风下均显著优于单通道方法。
- 能准确追踪移动说话人，适合真实会议场景应用。
我们提出一种基于时空联合建模的会议语音分角色识别流程，包含基于时差定位（TDOA）的语音段分割与基于说话人嵌入的聚类。该系统无需多通道训练数据，也不依赖麦克风数量或布局的先验知识，适用于紧凑麦克风阵列和分布式麦克风部署，仅需微调即可适配。由于在分割阶段对重叠语音具有更强处理能力，该方案在紧凑阵列与分布式麦克风两种场景下均显著优于单通道pyannote方法。此外，与完全依赖空间信息的方法不同，本系统可在说话人位置变化时仍正确追踪其身份。
原文摘要 · Abstract (English)
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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