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

The x-vector based deep neural network (DNN) embedding systems havedemonstrated effectiveness for text-independent speaker verification. Thispaper presents a multi-task learning architecture for training the speakerembedding DNN with the primary task of classifying the target speakers, and theauxiliary task of reconstructing the first- and higher-order statistics of theoriginal input utterance. The proposed training strategy aggregates both thesupervised and unsupervised learning into one framework to make the speakerembeddings more discriminative and robust. Experiments are carried out usingthe NIST SRE16 evaluation dataset and the VOiCES dataset. The resultsdemonstrate that our proposed method outperforms the original x-vector approachwith very low additional complexity added.

Multi-Task Learning with High-Order Statistics for X-vector based Text-Independent Speaker Verification