This paper presents exploratory research on temporally dynamic patterns of vowel nasalization from two speakers of Arabana. To derive a dynamic measure of nasality, we use gradient tree boosting algorithms to statistically learn the mapping between acoustics and vowel nasality in a speaker-specific manner. Three primary findings emerge: (1) NVN contexts exhibit nasalization throughout the entirety of the vowel interval, and we propose that a similar co-articulatory realization previously acted to resist diachronic change in this environment; (2) anticipatory vowel nasalization is nearly as extensive as carryover vowel nasalization, which is contrary to previous claims; and (3) the degree of vowel nasalization in word-initial contexts is relatively high, even in the #_C environment, suggesting that the ongoing sound change *#ŋa > #a has involved the loss of the oral constriction associated with ŋ but not the complete loss of the velum gesture.
Carignan, C., Chen, J., Harvey, M., Stockigt, C., Simpson, J., & Strangways, S. (2023). An investigation of the dynamics of vowel nasalization in Arabana using machine learning of acoustic features. Laboratory Phonology: Journal of the Association for Laboratory Phonology, 14(1), pp. 1–31. DOI: https://doi.org/10.16995/labphon.9152