  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 allophonic distribution in
English. The network successfully learns the allophonic alternation: the
network's generated speech signal contains the conditional distribution of
aspiration duration. The paper proposes a technique for establishing the
network's internal representations that identifies latent variables that
correspond to, for example, presence of [s] and its spectral properties. By
manipulating these variables, we actively control the presence of [s] and its
frication amplitude in the generated outputs. This suggests that the network
learns to use latent variables as an approximation of phonetic and phonological
representations. Crucially, we observe that the dependencies learned in
training extend beyond the training interval, which allows for additional
exploration of learning representations. The paper also discusses how the
network's architecture and innovative outputs resemble and differ from
linguistic behavior in language acquisition, speech disorders, and speech
errors, and how well-understood dependencies in speech data can help us
interpret how neural networks learn their representations.
