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

The speech signal is a consummate example of time-series data. The acousticsof the signal change over time, sometimes dramatically. Yet, the most commontype of comparison we perform in phonetics is between instantaneous acousticmeasurements, such as formant values. In the present paper, I discuss theconcept of absement as a quantification of differences between two time-series.I then provide an experimental example of absement applied to phonetic analysisfor human and/or computer speech recognition. The experiment is atemplate-based speech recognition task, using dynamic time warping to comparethe acoustics between recordings of isolated words. A recognition accuracy of57.9% was achieved. The results of the experiment are discussed in terms ofusing absement as a tool, as well as the implications of using acoustics-onlymodels of spoken word recognition with the word as the smallest discretelinguistic unit.

Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech data