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

As automatic speech recognition-based applications become increasingly common in a wide variety of market segments, thereis a growing need to support more languages. However, for many languages, the language resources needed to train speechrecognition engines are either limited or completely non-existent, and the process of acquiring or constructing new languageresources is both long and costly. This paper suggests a methodology that enables Phonetic Search Keyword Spotting to beimplemented in a large speech database of any given under-resourced language using cross-language phoneme mappings toanother language. The phoneme mapping enables a speech recognition engine from a sufficiently resourced and well-trainedsource language to be used for phoneme recognition in the new target language. The keyword search is then performed overa lattice of target language phonemes. Three cross-language phoneme mapping techniques are examined: knowledge-based,data-driven and phoneme recognition performance-based. The results suggest that Phonetic Search Keyword Spotting basedon the cross-language phoneme mapping approach proposed herein can serve as a quick initial solution for validating keywordspotting applications in new, under-resourced languages.

Cross-language phoneme mapping for phonetic search keyword spotting in continuous speech of under-resourced languages