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

Prosodic cues in conversational speech aid listeners in discerning a message.We investigate whether acoustic cues in spoken dialogue can be used to identifythe importance of individual words to the meaning of a conversation turn.Individuals who are Deaf and Hard of Hearing often rely on real-time captionsin live meetings. Word error rate, a traditional metric for evaluatingautomatic speech recognition, fails to capture that some words are moreimportant for a system to transcribe correctly than others. We present andevaluate neural architectures that use acoustic features for 3-class wordimportance prediction. Our model performs competitively againststate-of-the-art text-based word-importance prediction models, and itdemonstrates particular benefits when operating on imperfect ASR output.

Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues