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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Speaker diarization is a task concerned with partitioning an audio recordingby speaker identity. End-to-end neural diarization with encoder-decoder basedattractor calculation (EEND-EDA) aims to solve this problem by directlyoutputting diarization results for a flexible number of speakers. Currently,the EDA module responsible for generating speaker-wise attractors isconditioned on zero vectors providing no relevant information to the network.In this work, we extend EEND-EDA by replacing the input zero vectors to thedecoder with learned conversational summary representations. The updated EDAmodule sequentially generates speaker-wise attractors based on utterance-levelinformation. We propose three methods to initialize the summary vector andconduct an investigation into varying input recording lengths. On a range ofpublicly available test sets, our model achieves an absolute DER performanceimprovement of 1.90 % when compared to the baseline.

Improving End-to-End Neural Diarization Using Conversational Summary Representations