Local and non-local dependency learning and emergence of rule-like representations in speech data by Deep Convolutional Generative Adversarial Networks

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-like rule-based morphophonological processes emerge in a deep convolutional architecture. Acquisition of speech has recent...

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Veröffentlicht in:	arXiv.org 2021-07
1. Verfasser:	Beguš, Gašper
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Computer Science - Computation and Language Computer Science - Learning Dependence Generative adversarial networks Human subjects Machine learning Phonology Representations Speech Training
Online-Zugang:	Volltext
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