# Exploring How Generative Adversarial Networks Learn Phonological Representations

arXiv: https://arxiv.org/abs/2305.12501 · HTML: https://mlsanity.com/paper/2305.12501/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 24 of 24)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies](https://mlsanity.com/paper/1611.01368/) (2016)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State](https://mlsanity.com/paper/1903.03260/) (2019)
- [The Zero Resource Speech Challenge 2021: Spoken language modelling](https://mlsanity.com/paper/2104.14700/) (2021)
- [The Zero Resource Speech Challenge 2017](https://mlsanity.com/paper/1712.04313/) (2017)
- [The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units](https://mlsanity.com/paper/2010.05967/) (2020)
- [The Zero Resource Speech Challenge 2019: TTS without T](https://mlsanity.com/paper/1904.11469/) (2019)
- [Representations of language in a model of visually grounded speech signal](https://mlsanity.com/paper/1702.01991/) (2017)
- [Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder](https://mlsanity.com/paper/1603.00982/) (2016)
- [CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks](https://mlsanity.com/paper/2006.02951/) (2020)
- [Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks](https://mlsanity.com/paper/2006.03965/) (2020)
- [Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication](https://mlsanity.com/paper/2009.06110/) (2020)
- [From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning](https://mlsanity.com/paper/1610.03342/) (2016)
- [Interpreting intermediate convolutional layers of generative CNNs trained on waveforms](https://mlsanity.com/paper/2104.09489/) (2021)

## Built on by (most cited first, 4 of 4)

- [Disentanglement in a GAN for Unconditional Speech Synthesis](https://mlsanity.com/paper/2307.01673/) (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)
- [A Technique for Isolating Lexically-Independent Phonetic Dependencies in Generative CNNs](https://mlsanity.com/paper/2506.09218/) (2025)

## Abstract (arXiv)

This paper explores how Generative Adversarial Networks (GANs) learn representations of phonological phenomena. We analyze how GANs encode contrastive and non-contrastive nasality in French and English vowels by applying the ciwGAN architecture (Begus 2021a). Begus claims that ciwGAN encodes linguistically meaningful representations with categorical variables in its latent space and manipulating the latent variables shows an almost one to one corresponding control of the phonological features in ciwGAN's generated outputs. However, our results show an interactive effect of latent variables on the features in the generated outputs, which suggests the learned representations in neural networks are different from the phonological representations proposed by linguists. On the other hand, ciwGAN is able to distinguish contrastive and noncontrastive features in English and French by encoding them differently. Comparing the performance of GANs learning from different languages results in a better understanding of what language specific features contribute to developing language specific phonological representations. We also discuss the role of training data frequencies in phonological feature learning.