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 presents a novel approach for enhancing the multiple sets ofacoustic patterns automatically discovered from a given corpus. In a previouswork it was proposed that different HMM configurations (number of states permodel, number of distinct models) for the acoustic patterns form atwo-dimensional space. Multiple sets of acoustic patterns automaticallydiscovered with the HMM configurations properly located on different pointsover this two-dimensional space were shown to be complementary to one another,jointly capturing the characteristics of the given corpus. By representing thegiven corpus as sequences of acoustic patterns on different HMM sets, thepattern indices in these sequences can be relabeled considering the contextconsistency across the different sequences. Good improvements were observed inpreliminary experiments of pattern spoken term detection (STD) performed onboth TIMIT and Mandarin Broadcast News with such enhanced patterns.

Enhancing Automatically Discovered Multi-level Acoustic Patterns Considering Context Consistency With Applications in Spoken Term Detection