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 present a Diffusion GAN based approach (Prosodic Diff-TTS)to generate the corresponding high-fidelity speech based on the styledescription and content text as an input to generate speech samples within only4 denoising steps. It leverages the novel conditional prosodic layernormalization to incorporate the style embeddings into the multi head attentionbased phoneme encoder and mel spectrogram decoder based generator architectureto generate the speech. The style embedding is generated by fine tuning thepretrained BERT model on auxiliary tasks such as pitch, speaking speed,emotion,gender classifications. We demonstrate the efficacy of our proposedarchitecture on multi-speaker LibriTTS and PromptSpeech datasets, usingmultiple quantitative metrics that measure generated accuracy and MOS.

Style Description based Text-to-Speech with Conditional Prosodic Layer Normalization based Diffusion GAN