CiwaGAN: Articulatory information exchange
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 14 Sep 2023.
Builds on
17 of 26 references.
References · 17
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
- 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
- 2018Adversarial Audio Synthesiscited by 225
- 2020CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networkscited by 15
- 2020Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networkscited by 12
- 2023Deep Speech Synthesis from MRI-Based Articulatory Representationscited by 12
- 2022Deep Speech Synthesis from Articulatory Representationscited by 10
- 2020Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplicationcited by 9
- 2020Local and non-local dependency learning and emergence of rule-like representations in speech data by Deep Convolutional Generative Adversarial Networkscited by 7
- 2021EMA2S: An End-to-End Multimodal Articulatory-to-Speech Systemcited by 5
- 2022Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech datacited by 5
5 more
- 2022Self-supervised speech unit discovery from articulatory and acoustic features using VQ-VAEcited by 5
- 2023Approaching an unknown communication system by latent space exploration and causal inferencecited by 4
- 2022Articulation GAN: Unsupervised modeling of articulatory learningcited by 3
- 2021The Mirrornet : Learning Audio Synthesizer Controls Inspired by Sensorimotor Interactioncited by 2
- 2022Learning to Compute the Articulatory Representations of Speech with the MIRRORNETcited by 2
Built on by
Citing papers per year: 2024: 1.
1 paper.