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

Prosody is a rich information source in natural language, serving as a markerfor phenomena such as contrast. In order to make this information available todownstream tasks, we need a way to detect prosodic events in speech. We proposea new model for pitch accent detection, inspired by the work of Stehwien et al.(2018), who presented a CNN-based model for this task. Our model makes greateruse of context by using full utterances as input and adding an LSTM layer. Wefind that these innovations lead to an improvement from 87.5% to 88.7% accuracyon pitch accent detection on American English speech in the Boston UniversityRadio News Corpus, a state-of-the-art result. We also find that a simplebaseline that just predicts a pitch accent on every content word yields 82.2%accuracy, and we suggest that this is the appropriate baseline for this task.Finally, we conduct ablation tests that show pitch is the most importantacoustic feature for this task and this corpus.

The role of context in neural pitch accent detection in English