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

We present a freely available speech corpus for the Uzbek language and reportpreliminary automatic speech recognition (ASR) results using both the deepneural network hidden Markov model (DNN-HMM) and end-to-end (E2E)architectures. The Uzbek speech corpus (USC) comprises 958 different speakerswith a total of 105 hours of transcribed audio recordings. To the best of ourknowledge, this is the first open-source Uzbek speech corpus dedicated to theASR task. To ensure high quality, the USC has been manually checked by nativespeakers. We first describe the design and development procedures of the USC,and then explain the conducted ASR experiments in detail. The experimentalresults demonstrate promising results for the applicability of the USC for ASR.Specifically, 18.1% and 17.4% word error rates were achieved on the validationand test sets, respectively. To enable experiment reproducibility, we share theUSC dataset, pre-trained models, and training recipes in our GitHub repository.

USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments