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

With the rise of low power speech-enabled devices, there is a growing demandto quickly produce models for recognizing arbitrary sets of keywords. As withmany machine learning tasks, one of the most challenging parts in the modelcreation process is obtaining a sufficient amount of training data. In thispaper, we explore the effectiveness of synthesized speech data in trainingsmall, spoken term detection models of around 400k parameters. Instead oftraining such models directly on the audio or low level features such as MFCCs,we use a pre-trained speech embedding model trained to extract useful featuresfor keyword spotting models. Using this speech embedding, we show that a modelwhich detects 10 keywords when trained on only synthetic speech is equivalentto a model trained on over 500 real examples. We also show that a model withoutour speech embeddings would need to be trained on over 4000 real examples toreach the same accuracy.

Training Keyword Spotters with Limited and Synthesized Speech Data