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

In the paper we present an architecture of a keyword spotting (KWS) systemthat is based on modern neural networks, yields good performance on varioustypes of speech data and can run very fast. We focus mainly on the last aspectand propose optimizations for all the steps required in a KWS design: signalprocessing and likelihood computation, Viterbi decoding, spot candidatedetection and confidence calculation. We present time and memory efficientmodelling by bidirectional feedforward sequential memory networks (analternative to recurrent nets) either by standard triphones or so calledquasi-monophones, and an entirely forward decoding of speech frames (withminimal need for look back). Several variants of the proposed scheme areevaluated on 3 large Czech datasets (broadcast, internet and telephone, 17hours in total) and their performance is compared by Detection Error Tradeoff(DET) diagrams and real-time (RT) factors. We demonstrate that the completesystem can run in a single pass with a RT factor close to 0.001 if alloptimizations (including a GPU for likelihood computation) are applied.

Very Fast Keyword Spotting System with Real Time Factor below 0.01