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

Due to the high performance of multi-channel speech processing, we can usethe outputs from a multi-channel model as teacher labels when training asingle-channel model with knowledge distillation. To the contrary, it is alsoknown that single-channel speech data can benefit multi-channel models bymixing it with multi-channel speech data during training or by using it formodel pretraining. This paper focuses on speaker diarization and proposes toconduct the above bi-directional knowledge transfer alternately. We firstintroduce an end-to-end neural diarization model that can handle both single-and multi-channel inputs. Using this model, we alternately conduct i) knowledgedistillation from a multi-channel model to a single-channel model and ii)finetuning from the distilled single-channel model to a multi-channel model.Experimental results on two-speaker data show that the proposed method mutuallyimproved single- and multi-channel speaker diarization performances.

Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization