基于多阶段聚类的极高效实时流式与完全端上说话人日志
Highly Efficient Real-Time Streaming and Fully On-Device Speaker Diarization with Multi-Stage Clustering
AI总结：
本文提出一种多阶段聚类策略，针对不同长度输入使用不同聚类算法，并支持配置计算复杂度上限，以解决流式端上说话人日志系统在CPU、内存和电池资源受限情况下的多重挑战。
摘要：
尽管近期说话人日志的研究进展主要集中在提升日志结果的质量上，但对提升日志系统效率的兴趣也日益增长。本文证明了一种多阶段聚类策略，该策略对输入使用不同聚类算法，能够应对端上说话人日志应用的多重挑战。具体而言，使用后备聚类器处理短输入；使用主聚类器处理中等长度输入；使用预聚类器压缩长输入，然后再由主聚类器处理。主聚类器和预聚类器均可配置计算复杂度上限，以适应具有不同资源限制的设备。这种多阶段聚类策略对于流式端上说话人日志系统至关重要，因为在此类系统中，CPU、内存和电池预算非常紧张。
英文摘要：
While recent research advances in speaker diarization mostly focus on improving the quality of diarization results, there is also an increasing interest in improving the efficiency of diarization systems. In this paper, we demonstrate that a multi-stage clustering strategy that uses different clustering algorithms for input of different lengths can address multi-faceted challenges of on-device speaker diarization applications. Specifically, a fallback clusterer is used to handle short-form inputs; a main clusterer is used to handle medium-length inputs; and a pre-clusterer is used to compress long-form inputs before they are processed by the main clusterer. Both the main clusterer and the pre-clusterer can be configured with an upper bound of the computational complexity to adapt to devices with different resource constraints. This multi-stage clustering strategy is critical for streaming on-device speaker diarization systems, where the budgets of CPU, memory and battery are tight.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
