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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Speaker recognition deals with recognizing speakers by their speech. Mostspeaker recognition systems are built upon two stages, the first stage extractslow dimensional correlation embeddings from speech, and the second performs theclassification task. The robustness of a speaker recognition system mainlydepends on the extraction process of speech embeddings, which are primarilypre-trained on a large-scale dataset. As the embedding systems are pre-trained,the performance of speaker recognition models greatly depends on domainadaptation policy, which may reduce if trained using inadequate data. Thispaper introduces a speaker recognition strategy dealing with unlabeled data,which generates clusterable embedding vectors from small fixed-size speechframes. The unsupervised training strategy involves an assumption that a smallspeech segment should include a single speaker. Depending on such a belief, apairwise constraint is constructed with noise augmentation policies, used totrain AutoEmbedder architecture that generates speaker embeddings. Withoutrelying on domain adaption policy, the process unsupervisely producesclusterable speaker embeddings, termed unsupervised vectors (u-vectors). Theevaluation is concluded in two popular speaker recognition datasets for Englishlanguage, TIMIT, and LibriSpeech. Also, a Bengali dataset is included toillustrate the diversity of the domain shifts for speaker recognition systems.Finally, we conclude that the proposed approach achieves satisfactoryperformance using pairwise architectures.

U-vectors: Generating clusterable speaker embedding from unlabeled data