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

This paper introduces the system submitted by the Yidun NISP team to thevideo keyword wakeup challenge. We propose a mandarin keyword spotting system(KWS) with several novel and effective improvements, including a big backbone(B) model, a keyword biasing (B) mechanism and the introduction of syllablemodeling units (S). By considering this, we term the total system BBS-KWS as anabbreviation. The BBS-KWS system consists of an end-to-end automatic speechrecognition (ASR) module and a KWS module. The ASR module converts speechfeatures to text representations, which applies a big backbone network to theacoustic model and takes syllable modeling units into consideration as well. Inaddition, the keyword biasing mechanism is used to improve the recall rate ofkeywords in the ASR inference stage. The KWS module applies multiple criteriato determine the absence or presence of the keywords, such as multi-stagematching, fuzzy matching, and connectionist temporal classification (CTC)prefix score. To further improve our system, we conduct semi-supervisedlearning on the CN-Celeb dataset for better generalization. In the VKW task,the BBS-KWS system achieves significant gains over the baseline and won thefirst place in two tracks.

BBS-KWS:The Mandarin Keyword Spotting System Won the Video Keyword Wakeup Challenge