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

Neural speech synthesis models have recently demonstrated the ability tosynthesize high quality speech for text-to-speech and compression applications.These new models often require powerful GPUs to achieve real-time operation, sobeing able to reduce their complexity would open the way for many newapplications. We propose LPCNet, a WaveRNN variant that combines linearprediction with recurrent neural networks to significantly improve theefficiency of speech synthesis. We demonstrate that LPCNet can achievesignificantly higher quality than WaveRNN for the same network size and thathigh quality LPCNet speech synthesis is achievable with a complexity under 3GFLOPS. This makes it easier to deploy neural synthesis applications onlower-power devices, such as embedded systems and mobile phones.

LPCNet: Improving Neural Speech Synthesis Through Linear Prediction