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

Speaker verification (SV) suffers from unsatisfactory performance infar-field scenarios due to environmental noise andthe adverse impact of roomreverberation. This work presents a benchmark of multichannel speechenhancement for far-fieldspeaker verification. One approach is a deep neuralnetwork-based, and the other is a combination of deep neural network andsignalprocessing. We integrated a DNN architecture with signal processing techniquesto carry out various experiments. Ourapproach is compared to the existingstate-of-the-art approaches. We examine the importance of enrollment inpre-processing,which has been largely overlooked in previous studies.Experimental evaluation shows that pre-processing can improve the SVperformanceas long as the enrollment files are processed similarly to the test data andthat test and enrollment occur within similarSNR ranges. Considerableimprovement is obtained on the generated and all the noise conditions of theVOiCES dataset.

How to Leverage DNN-based speech enhancement for multi-channel speaker verification?