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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting. Most prior work require either parallel speech corpora or enough amount of training data from a target speaker. However, we convert an arbitrary sentences of an arbitrary source speaker to target speakeru0027s given only one target speaker training utterance. To achieve this, we formulate the problem as learning disentangled speaker-specific and context-specific representations and follow the idea of [1] which uses Factorized Hierarchical Variational Autoencoder (FHVAE). After training FHVAE on multi-speaker training data, given arbitrary source and target speakersu0027 utterance, we estimate those latent representations and then reconstruct the desired utterance of converted voice to that of target speaker. We investigate the effectiveness of the approach by conducting voice conversion experiments with varying size of training utterances and it was able to achieve reasonable performance with even just one training utterance. We also examine the speech representation and show that World vocoder outperforms Short-time Fourier Transform (STFT) used in [1]. Finally, in the subjective tests, for one language and cross-lingual voice conversion, our approach achieved significantly better or comparable results compared to VAE-STFT and GMM baselines in speech quality and similarity.

Investigation of Using Disentangled and Interpretable Representations for One-shot Cross-lingual Voice Conversion