Optimizing an automatic creaky voice detection method for Australian English-speaking females
|Hannah White (Macquarie University, Australia), Joshua Penney (Macquarie University, Australia), Andy Gibson (Macquarie University, Australia), Anita Szakay (Macquarie University, Australia), Felicity Cox (Macquarie University, Australia)|
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.