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

While discrete latent variable models have had great success inself-supervised learning, most models assume that frames are independent. Dueto the segmental nature of phonemes in speech perception, modeling dependenciesamong latent variables at the frame level can potentially improve the learnedrepresentations on phonetic-related tasks. In this work, we assume Markoviandependencies among latent variables, and propose to learn speechrepresentations with neural hidden Markov models. Our general framework allowsus to compare to self-supervised models that assume independence, while keepingthe number of parameters fixed. The added dependencies improve theaccessibility of phonetic information, phonetic segmentation, and the clusterpurity of phones, showcasing the benefit of the assumed dependencies.

Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models