English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Clustering-based speaker diarization has stood firm as one of the majorapproaches in reality, despite recent development in end-to-end diarization.However, clustering methods have not been explored extensively for speakerdiarization. Commonly-used methods such as k-means, spectral clustering, andagglomerative hierarchical clustering only take into account properties such asproximity and relative densities. In this paper we propose to viewclustering-based diarization as a community detection problem. By doing so thetopological structure is considered. This work has four major contributions.First it is shown that Leiden community detection algorithm significantlyoutperforms the previous methods on the clustering of speaker-segments. Second,we propose to use uniform manifold approximation to reduce dimension whileretaining global and local topological structure. Third, a masked filteringapproach is introduced to extract "clean" speaker embeddings. Finally, thecommunity structure is applied to an end-to-end post-processing network toobtain diarization results. The final system presents a relative DER reductionof up to 70 percent. The breakdown contribution of each component is analyzed.

Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure