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

In this paper, we propose a universal background model, named universalbackground sparse coding (UBSC), for speaker verification. The proposed methodtrains an ensemble of clusterings by data resampling, and produces sparse codesfrom the clusterings by one-nearest-neighbor optimization plus binarization.The main advantage of UBSC is that it does not suffer from local minima anddoes not make Gaussian assumptions on data distributions. We evaluated UBSC ona clean speech corpus---TIMIT. We used the cosine similarity and inner productsimilarity as the scoring methods of a trial. Experimental results show thatUBSC is comparable to Gaussian mixture model.

An Investigation of Universal Background Sparse Coding Based Speaker Verification on TIMIT