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

By representing speaker characteristic as a single fixed-length vectorextracted solely from speech, we can train a neural multi-speaker speechsynthesis model by conditioning the model on those vectors. This model can alsobe adapted to unseen speakers regardless of whether the transcript ofadaptation data is available or not. However, this setup restricts the speakercomponent to just a single bias vector, which in turn limits the performance ofadaptation process. In this study, we propose a novel speech synthesis model,which can be adapted to unseen speakers by fine-tuning part of or all of thenetwork using either transcribed or untranscribed speech. Our methodologyessentially consists of two steps: first, we split the conventional acousticmodel into a speaker-independent (SI) linguistic encoder and a speaker-adaptive(SA) acoustic decoder; second, we train an auxiliary acoustic encoder that canbe used as a substitute for the linguistic encoder whenever linguistic featuresare unobtainable. The results of objective and subjective evaluations show thatadaptation using either transcribed or untranscribed speech with ourmethodology achieved a reasonable level of performance with an extremelylimited amount of data and greatly improved performance with more data.Surprisingly, adaptation with untranscribed speech surpassed the transcribedcounterpart in the subjective test, which reveals the limitations of theconventional acoustic model and hints at potential directions for improvements.

A Unified Speaker Adaptation Method for Speech Synthesis using Transcribed and Untranscribed Speech with Backpropagation