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

The lack of clean speech is a practical challenge to the development ofspeech enhancement systems, which means that there is an inevitable mismatchbetween their training criterion and evaluation metric. In response to thisunfavorable situation, we propose a training and inference strategy thatadditionally uses enhanced speech as a target by improving the previouslyproposed noisy-target training (NyTT). Because homogeneity between in-domainnoise and extraneous noise is the key to the effectiveness of NyTT, we trainvarious student models by remixing 1) the teacher model's estimated speech andnoise for enhanced-target training or 2) raw noisy speech and the teachermodel's estimated noise for noisy-target training. Experimental results showthat our proposed method outperforms several baselines, especially with theteacher/student inference, where predicted clean speech is derived successivelythrough the teacher and final student models.

A Training and Inference Strategy Using Noisy and Enhanced Speech as Target for Speech Enhancement without Clean Speech