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

This paper presents the sequence-to-sequence (seq2seq) baseline system forthe voice conversion challenge (VCC) 2020. We consider a naive approach forvoice conversion (VC), which is to first transcribe the input speech with anautomatic speech recognition (ASR) model, followed using the transcriptions togenerate the voice of the target with a text-to-speech (TTS) model. We revisitthis method under a sequence-to-sequence (seq2seq) framework by utilizingESPnet, an open-source end-to-end speech processing toolkit, and the manywell-configured pretrained models provided by the community. Officialevaluation results show that our system comes out top among the participatingsystems in terms of conversion similarity, demonstrating the promising abilityof seq2seq models to convert speaker identity. The implementation is madeopen-source at: https://github.com/espnet/espnet/tree/master/egs/vcc20.

The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS