CiwaGAN: Articulatory information exchange
Gašper Beguš Thomas Lu Alan Zhou Peter Wu Gopala K. Anumanchipalli
Humans encode information into sounds by controlling articulators and decode
information from sounds using the auditory apparatus. This paper introduces
CiwaGAN, a model of human spoken language acquisition that combines
unsupervised articulatory modeling with an unsupervised model of information
exchange through the auditory modality. While prior research includes
unsupervised articulatory modeling and information exchange separately, our
model is the first to combine the two components. The paper also proposes an
improved articulatory model with more interpretable internal representations.
The proposed CiwaGAN model is the most realistic approximation of human spoken
language acquisition using deep learning. As such, it is useful for cognitively
plausible simulations of the human speech act.