Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks.

ABSTRACT: Training deep neural networks on well-understood dependencies in speech data can provide new insights into how they learn internal representations. This paper argues that acquisition of speech can be modeled as a dependency between random space and generated speech data in the Generative Adversarial Network architecture and proposes a methodology to uncover the network's internal representations that correspond to phonetic and phonological properties. The Generative Adversarial architecture is uniquely appropriate for modeling phonetic and phonological learning because the network is trained on unannotated raw acoustic data and learning is unsupervised without any language-specific assumptions or pre-assumed levels of abstraction. A Generative Adversarial Network was trained on an allophon

SUBMITTER: Begus G 

PROVIDER: S-EPMC7861218 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature