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

Recently, end-to-end neural diarization (EEND) is introduced and achievespromising results in speaker-overlapped scenarios. In EEND, speaker diarizationis formulated as a multi-label prediction problem, where speaker activities areestimated independently and their dependency are not well considered. Toovercome these disadvantages, we employ the power set encoding to reformulatespeaker diarization as a single-label classification problem and propose theoverlap-aware EEND (EEND-OLA) model, in which speaker overlaps and dependencycan be modeled explicitly. Inspired by the success of two-stage hybrid systems,we further propose a novel Two-stage OverLap-aware Diarization framework (TOLD)by involving a speaker overlap-aware post-processing (SOAP) model toiteratively refine the diarization results of EEND-OLA. Experimental resultsshow that, compared with the original EEND, the proposed EEND-OLA achieves a14.39% relative improvement in terms of diarization error rates (DER), andutilizing SOAP provides another 19.33% relative improvement. As a result, ourmethod TOLD achieves a DER of 10.14% on the CALLHOME dataset, which is a newstate-of-the-art result on this benchmark to the best of our knowledge.

TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization