# Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks

arXiv: https://arxiv.org/abs/2006.03965 · HTML: https://mlsanity.com/paper/2006.03965/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Interpreting intermediate convolutional layers of generative CNNs trained on waveforms](https://mlsanity.com/paper/2104.09489/) (2021)
- [Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks](https://mlsanity.com/paper/2305.01626/) (2023)
- [Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data](https://mlsanity.com/paper/2203.11476/) (2022)
- [Approaching an unknown communication system by latent space exploration and causal inference](https://mlsanity.com/paper/2303.10931/) (2023)
- [Exploring How Generative Adversarial Networks Learn Phonological Representations](https://mlsanity.com/paper/2305.12501/) (2023)
- [Disentanglement in a GAN for Unconditional Speech Synthesis](https://mlsanity.com/paper/2307.01673/) (2023)
- [Interpreting intermediate convolutional layers in unsupervised acoustic word classification](https://mlsanity.com/paper/2110.02375/) (2021)
- [GAN You Hear Me? Reclaiming Unconditional Speech Synthesis from Diffusion Models](https://mlsanity.com/paper/2210.05271/) (2022)
- [Articulation GAN: Unsupervised modeling of articulatory learning](https://mlsanity.com/paper/2210.15173/) (2022)
- [CiwaGAN: Articulatory information exchange](https://mlsanity.com/paper/2309.07861/) (2023)
- [Unsupervised Learning and Representation of Mandarin Tonal Categories by a Generative CNN](https://mlsanity.com/paper/2509.17859/) (2025)
- [Deciphering Assamese Vowel Harmony with Featural InfoWaveGAN](https://mlsanity.com/paper/2407.06547/) (2024)

## Abstract (arXiv)

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.