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Local and non-local dependency learning and emergence of rule-like representations in speech data by Deep Convolutional Generative Adversarial Networks
- Publication Year :
- 2022
- Publisher :
- Open Science Framework, 2022.
-
Abstract
- 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 recently been modeled as a dependency between latent space and data generated by GANs in Begu\v{s} (2020b; arXiv:2006.03965), who models learning of a simple local allophonic distribution. We extend this approach to test learning of local and non-local phonological processes that include approximations of morphological processes. We further parallel outputs of the model to results of a behavioral experiment where human subjects are trained on the data used for training the GAN network. Four main conclusions emerge: (i) the networks provide useful information for computational models of speech acquisition even if trained on a comparatively small dataset of an artificial grammar learning experiment; (ii) local processes are easier to learn than non-local processes, which matches both behavioral data in human subjects and typology in the world's languages. This paper also proposes (iii) how we can actively observe the network's progress in learning and explore the effect of training steps on learning representations by keeping latent space constant across different training steps. Finally, this paper shows that (iv) the network learns to encode the presence of a prefix with a single latent variable; by interpolating this variable, we can actively observe the operation of a non-local phonological process. The proposed technique for retrieving learning representations has general implications for our understanding of how GANs discretize continuous speech data and suggests that rule-like generalizations in the training data are represented as an interaction between variables in the network's latent space.<br />Comment: In press at Computer Speech & Language
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Dependency (UML)
Speech acquisition
Artificial grammar learning
Computer science
02 engineering and technology
Latent variable
01 natural sciences
Theoretical Computer Science
Machine Learning (cs.LG)
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
010301 acoustics
Computational model
Computer Science - Computation and Language
business.industry
020206 networking & telecommunications
Human-Computer Interaction
Variable (computer science)
Phonological rule
Artificial intelligence
business
Computation and Language (cs.CL)
Software
Generative grammar
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....a4fa4101899e688307d2ef22c83124a7
- Full Text :
- https://doi.org/10.17605/osf.io/a9wmy