42 citations · 157 across the 18 of their papers we have counts for
5 papers · 1 filter
Deep Sound Change: Deep and Iterative Learning, Convolutional Neural Networks, and Language Change
Gašper Beguš
This paper proposes a framework for modeling sound change that combines deep learning and iterative learning. Acquisition and transmission of speech is modeled by training generati…
Local and non-local dependency learning and emergence of rule-like representations in speech data by Deep Convolutional Generative Adversarial Networks
Gašper Beguš
This paper argues that training GANs on local and non-local dependencies in speech data offers insights into how deep neural networks discretize continuous data and how symbolic-li…
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication
Gašper Beguš
This paper models unsupervised learning of an identity-based pattern (or copying) in speech called reduplication from raw continuous data with deep convolutional neural networks. W…
Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks
Gašper Beguš
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 acquisiti…
CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks
Gašper Beguš
How can deep neural networks encode information that corresponds to words in human speech into raw acoustic data? This paper proposes two neural network architectures for modeling…