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/

Zendy Plus

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

Zendy Tools

Zendy Open

Deep speaker embedding has achieved state-of-the-art performance in speakerrecognition. A potential problem of these embedded vectors (called `x-vectors')are not Gaussian, causing performance degradation with the famous PLDA back-endscoring. In this paper, we propose a regularization approach based onVariational Auto-Encoder (VAE). This model transforms x-vectors to a latentspace where mapped latent codes are more Gaussian, hence more suitable for PLDAscoring.

VAE-based regularization for deep speaker embedding