27 citations · 27 across the 2 of their papers we have counts for
3 papers
cs.SD2021
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales
Jacob Andreas, Gašper Beguš, Michael M. Bronstein +16
The past decade has witnessed a groundbreaking rise of machine learning for human language analysis, with current methods capable of automatically accurately recovering various asp…
cs.CL2020
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…
cs.CL2020★ 27 cited
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…