Neural network models of phonological learnability are said to learn the phonotactics of a language better than traditional mod-els of learnability[1]. Our paper explores whether the Featural InfoWaveGAN architecture (fiwGAN [2]; inspired by Wave-GAN [3] and InfoGAN [4]) can capture regressive vowel har-mony patterns when trained unsupervised on raw acoustic data without any supply of prosodic cues. We train the model with Assamese speech data recorded by 15 native speakers. As-samese is one of the few Indian languages that exhibit phono-logically regressive and word-bound vowel harmony. [+high, +ATR] vowels [i, u] trigger right-to-left harmony of [-ATR] vowels [ε, ɔ, ʊ] resulting in [e], [o], and [u], respectively. We analyze the outputs generated by the fiwGAN model and ob-serve that it learns the regressive directionality of harmony. It produces innovative items by stringing together vowels and con-sonants from the training dataset. It showcases its capability of learning the phonotactics of Assamese and iterative harmony patterns over a longer domain without any relevant prosodic in-formation in the output. We assume the model treats the out-puts as abstract prosodic units without external prosodic cues triggering vowel harmony.