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

Emotional Text-To-Speech (TTS) is an important task in the development ofsystems (e.g., human-like dialogue agents) that require natural and emotionalspeech. Existing approaches, however, only aim to produce emotional TTS forseen speakers during training, without consideration of the generalization tounseen speakers. In this paper, we propose ZET-Speech, a zero-shot adaptiveemotion-controllable TTS model that allows users to synthesize any speaker'semotional speech using only a short, neutral speech segment and the targetemotion label. Specifically, to enable a zero-shot adaptive TTS model tosynthesize emotional speech, we propose domain adversarial learning andguidance methods on the diffusion model. Experimental results demonstrate thatZET-Speech successfully synthesizes natural and emotional speech with thedesired emotion for both seen and unseen speakers. Samples are athttps://ZET-Speech.github.io/ZET-Speech-Demo/.

ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models