Creaky voice is a nonmodal phonation type that has various linguistic
and sociolinguistic functions. Manually annotating creaky voice for
phonetic analysis is time-consuming and labor-intensive. In recent
years, automatic tools for detecting creaky voice have been proposed,
which present the possibility for easier, faster and more consistent
creak identification. One of these proposed tools is a Creak Detector
algorithm that uses an automatic neural network taking its input from
several acoustic cues to identify creaky voice. Previous work has suggested
that the creak probability threshold at which this tool determines
an instance to be creaky may vary depending on the speaker population.
The present study investigates the optimal creak detection threshold
for female Australian English speakers.
Results show further
support for the practice of first finding the optimal threshold when
using the Creak Detection algorithm on new data sets. Additionally,
results show that accuracy of creaky voice detection using the Creak
Detection algorithm can be significantly improved by excluding non-sonorant
data.
Cite as: White, H., Penney, J., Gibson, A., Szakay, A., Cox, F. (2021) Optimizing an Automatic Creaky Voice Detection Method for Australian English Speaking Females. Proc. Interspeech 2021, 1384-1388, doi: 10.21437/Interspeech.2021-711
@inproceedings{white21_interspeech,
author={Hannah White and Joshua Penney and Andy Gibson and Anita Szakay and Felicity Cox},
title={{Optimizing an Automatic Creaky Voice Detection Method for Australian English Speaking Females}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={1384--1388},
doi={10.21437/Interspeech.2021-711}
}