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

Prosody plays an important role in characterizing the style of a speaker oran emotion, but most non-parallel voice or emotion style transfer algorithms donot convert any prosody information. Two major components of prosody are pitchand rhythm. Disentangling the prosody information, particularly the rhythmcomponent, from the speech is challenging because it involves breaking thesynchrony between the input speech and the disentangled speech representation.As a result, most existing prosody style transfer algorithms would need to relyon some form of text transcriptions to identify the content information, whichconfines their application to high-resource languages only. Recently,SpeechSplit has made sizeable progress towards unsupervised prosody styletransfer, but it is unable to extract high-level global prosody style in anunsupervised manner. In this paper, we propose AutoPST, which can disentangleglobal prosody style from speech without relying on any text transcriptions.AutoPST is an Autoencoder-based Prosody Style Transfer framework with athorough rhythm removal module guided by the self-expressive representationlearning. Experiments on different style transfer tasks show that AutoPST caneffectively convert prosody that correctly reflects the styles of the targetdomains.

Global Rhythm Style Transfer Without Text Transcriptions