  Traditional approaches for understanding phonological learning have
predominantly relied on curated text data. Although insightful, such approaches
limit the knowledge captured in textual representations of the spoken language.
To overcome this limitation, we investigate the potential of the Featural
InfoWaveGAN model to learn iterative long-distance vowel harmony using raw
speech data. We focus on Assamese, a language known for its phonologically
regressive and word-bound vowel harmony. We demonstrate that the model is adept
at grasping the intricacies of Assamese phonotactics, particularly iterative
long-distance harmony with regressive directionality. It also produced
non-iterative illicit forms resembling speech errors during human language
acquisition. Our statistical analysis reveals a preference for a specific
[+high,+ATR] vowel as a trigger across novel items, indicative of feature
learning. More data and control could improve model proficiency, contrasting
the universality of learning.
