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

Models based on attention mechanisms have shown unprecedented speechrecognition performance. However, they are computationally expensive andunnecessarily complex for keyword spotting, a task targeted to small-footprintdevices. This work explores the application of Lambda networks, an alternativeframework for capturing long-range interactions without attention, for thekeyword spotting task. We propose a novel \textit{ResNet}-based model byswapping the residual blocks by temporal Lambda layers. Furthermore, theproposed architecture is built upon uni-dimensional temporal convolutions thatfurther reduce its complexity. The presented model does not only reachstate-of-the-art accuracies on the Google Speech Commands dataset, but it is85% and 65% lighter than its Transformer-based (KWT) and convolutional (Res15)counterparts while being up to 100 times faster. To the best of our knowledge,this is the first attempt to explore the Lambda framework within the speechdomain and therefore, we unravel further research of new interfaces based onthis architecture.

Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda Networks