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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Accurately detecting voiced intervals in speech signals is a critical step inpitch tracking and has numerous applications. While conventional signalprocessing methods and deep learning algorithms have been proposed for thistask, their need to fine-tune threshold parameters for different datasets andlimited generalization restrict their utility in real-world applications. Toaddress these challenges, this study proposes a supervised voicing detectionmodel that leverages recorded laryngograph data. The model is based on adensely-connected convolutional recurrent neural network (DC-CRN), and trainedon data with reference voicing decisions extracted from laryngograph data sets.Pretraining is also investigated to improve the generalization ability of themodel. The proposed model produces robust voicing detection results,outperforming other strong baseline methods, and generalizes well to unseendatasets. The source code of the proposed model with pretraining is providedalong with the list of used laryngograph datasets to facilitate furtherresearch in this area.

Leveraging Laryngograph Data for Robust Voicing Detection in Speech