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

Effective fusion of multi-scale features is crucial for improving speakerverification performance. While most existing methods aggregate multi-scalefeatures in a layer-wise manner via simple operations, such as summation orconcatenation. This paper proposes a novel architecture called Enhanced Res2Net(ERes2Net), which incorporates both local and global feature fusion techniquesto improve the performance. The local feature fusion (LFF) fuses the featureswithin one single residual block to extract the local signal. The globalfeature fusion (GFF) takes acoustic features of different scales as input toaggregate global signal. To facilitate effective feature fusion in both LFF andGFF, an attentional feature fusion module is employed in the ERes2Netarchitecture, replacing summation or concatenation operations. A range ofexperiments conducted on the VoxCeleb datasets demonstrate the superiority ofthe ERes2Net in speaker verification. Code has been made publicly available athttps://github.com/alibaba-damo-academy/3D-Speaker.

An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification