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

Recent diarization technologies can be categorized into two approaches, i.e.,clustering and end-to-end neural approaches, which have different pros andcons. The clustering-based approaches assign speaker labels to speech regionsby clustering speaker embeddings such as x-vectors. While it can be seen as acurrent state-of-the-art approach that works for various challenging data withreasonable robustness and accuracy, it has a critical disadvantage that itcannot handle overlapped speech that is inevitable in natural conversationaldata. In contrast, the end-to-end neural diarization (EEND), which directlypredicts diarization labels using a neural network, was devised to handle theoverlapped speech. While the EEND, which can easily incorporate emergingdeep-learning technologies, has started outperforming the x-vector clusteringapproach in some realistic database, it is difficult to make it work for `long'recordings (e.g., recordings longer than 10 minutes) because of, e.g., its hugememory consumption. Block-wise independent processing is also difficult becauseit poses an inter-block label permutation problem, i.e., an ambiguity of thespeaker label assignments between blocks. In this paper, we propose a simplebut effective hybrid diarization framework that works with overlapped speechand for long recordings containing an arbitrary number of speakers. It modifiesthe conventional EEND framework to simultaneously output global speakerembeddings so that speaker clustering can be performed across blocks to solvethe permutation problem. With experiments based on simulated noisy reverberant2-speaker meeting-like data, we show that the proposed framework workssignificantly better than the original EEND especially when the input data islong.

Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds