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

The most common approach to speaker diarization is clustering of speakerembeddings. However, the clustering-based approach has a number of problems;i.e., (i) it is not optimized to minimize diarization errors directly, (ii) itcannot handle speaker overlaps correctly, and (iii) it has trouble adaptingtheir speaker embedding models to real audio recordings with speaker overlaps.To solve these problems, we propose the End-to-End Neural Diarization (EEND),in which a neural network directly outputs speaker diarization results given amulti-speaker recording. To realize such an end-to-end model, we formulate thespeaker diarization problem as a multi-label classification problem andintroduce a permutation-free objective function to directly minimizediarization errors. Besides its end-to-end simplicity, the EEND method canexplicitly handle speaker overlaps during training and inference. Just byfeeding multi-speaker recordings with corresponding speaker segment labels, ourmodel can be easily adapted to real conversations. We evaluated our method onsimulated speech mixtures and real conversation datasets. The results showedthat the EEND method outperformed the state-of-the-art x-vectorclustering-based method, while it correctly handled speaker overlaps. Weexplored the neural network architecture for the EEND method, and found thatthe self-attention-based neural network was the key to achieving excellentperformance. In contrast to conditioning the network only on its previous andnext hidden states, as is done using bidirectional long short-term memory(BLSTM), self-attention is directly conditioned on all the frames. Byvisualizing the attention weights, we show that self-attention captures globalspeaker characteristics in addition to local speech activity dynamics, makingit especially suitable for dealing with the speaker diarization problem.

End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification