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

We propose a novel method for emotion conversion in speech based on a chainedencoder-decoder-predictor neural network architecture. The encoder constructs alatent embedding of the fundamental frequency (F0) contour and the spectrum,which we regularize using the Large Diffeomorphic Metric Mapping (LDDMM)registration framework. The decoder uses this embedding to predict the modifiedF0 contour in a target emotional class. Finally, the predictor uses theoriginal spectrum and the modified F0 contour to generate a correspondingtarget spectrum. Our joint objective function simultaneously optimizes theparameters of three model blocks. We show that our method outperforms theexisting state-of-the-art approaches on both, the saliency of emotionconversion and the quality of resynthesized speech. In addition, the LDDMMregularization allows our model to convert phrases that were not present intraining, thus providing evidence for out-of-sample generalization.

Multi-speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network