MK-SGC-SC：多核引导的稀疏图构建在谱聚类中的语音说话人分离
MK-SGC-SC: Multiple Kernel Guided Sparse Graph Construction in Spectral Clustering for Unsupervised Speaker Diarization
AI总结：
本文提出基于多核引导的稀疏图构建方法，用于无监督语音说话人分离，通过多项式核和反余弦核测量嵌入相似性，提升局部相似性，实现高性能的说话人区域分割。
摘要：
语音说话人分离旨在将音频记录分割成对应于个别说话人的区域。尽管无监督的语音说话人分离本身具有挑战性，但无需预训练或弱监督识别说话人区域的前景推动了聚类技术的研究。在本工作中，我们分享了一个值得注意的观察，即测量多个核相似性度量说话人嵌入，然后以原则性的方式构建稀疏图进行谱聚类，足以在完全无监督的设置中实现最先进的性能。具体来说，我们考虑了四个多项式核和一个一次反余弦核，用于测量说话人嵌入中的相似性，利用这些核以原则性的方式构建稀疏图，以强调局部相似性。实验显示，所提出的方法在DIHARD-III、AMI和VoxConverse语料库中的各种具有挑战性的环境中，优于无监督的语音说话人分离。为了鼓励进一步的研究，我们的实现可在https://github.com/nikhilraghav29/MK-SGC-SC上获得。
英文摘要：
Speaker diarization aims to segment audio recordings into regions corresponding to individual speakers. Although unsupervised speaker diarization is inherently challenging, the prospect of identifying speaker regions without pretraining or weak supervision motivates research on clustering techniques. In this work, we share the notable observation that measuring multiple kernel similarities of speaker embeddings to thereafter craft a sparse graph for spectral clustering in a principled manner is sufficient to achieve state-of-the-art performances in a fully unsupervised setting. Specifically, we consider four polynomial kernels and a degree one arccosine kernel to measure similarities in speaker embeddings, using which sparse graphs are constructed in a principled manner to emphasize local similarities. Experiments show the proposed approach excels in unsupervised speaker diarization over a variety of challenging environments in the DIHARD-III, AMI, and VoxConverse corpora. To encourage further research, our implementations are available at https://github.com/nikhilraghav29/MK-SGC-SC.
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。
