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/

Zendy Plus

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

Zendy Tools

Zendy Open

Previous works on neural text-to-speech (TTS) have been addressed on limitedspeed in training and inference time, robustness for difficult synthesisconditions, expressiveness, and controllability. Although several approachesresolve some limitations, there has been no attempt to solve all weaknesses atonce. In this paper, we propose STYLER, an expressive and controllable TTSframework with high-speed and robust synthesis. Our novel audio-text aligningmethod called Mel Calibrator and excluding autoregressive decoding enable rapidtraining and inference and robust synthesis on unseen data. Also, disentangledstyle factor modeling under supervision enlarges the controllability insynthesizing process leading to expressive TTS. On top of it, a novel noisemodeling pipeline using domain adversarial training and Residual Decodingempowers noise-robust style transfer, decomposing the noise without anyadditional label. Various experiments demonstrate that STYLER is more effectivein speed and robustness than expressive TTS with autoregressive decoding andmore expressive and controllable than reading style non-autoregressive TTS.Synthesis samples and experiment results are provided via our demo page, andcode is available publicly.

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech