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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 17 of 17 1 Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no access to speech data ... Begus, Gasper. - : Open Science Framework, 2022 BASE Show details 2 Deep Sound Change ... Begus, Gasper. - : Open Science Framework, 2021 BASE Show details 3 Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ... Andreas, Jacob; Beguš, Gašper; Bronstein, Michael M.. - : arXiv, 2021 BASE Show details 4 Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Beguš, Gašper. - : Underline Science Inc., 2021 BASE Show details 5 Interpreting intermediate convolutional layers of CNNs trained on raw speech ... Beguš, Gašper; Zhou, Alan. - : arXiv, 2021 BASE Show details 6 Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ... Begus, Gasper. - : Open Science Framework, 2021 BASE Show details 7 Interpreting intermediate convolutional layers in unsupervised acoustic word classification ... Beguš, Gašper; Zhou, Alan. - : arXiv, 2021 BASE Show details 8 Generative Adversarial Phonology: Modeling unsupervised phonetic and phonological learning with neural networks ... Beguš, Gašper. - : arXiv, 2020 BASE Show details 9 Local and non-local dependency learning and emergence of rule-like representations in speech data by Deep Convolutional Generative Adversarial Networks ... Beguš, Gašper. - : arXiv, 2020 BASE Show details 10 Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication ... Beguš, Gašper. - : arXiv, 2020 BASE Show details 11 Deep Sound Change: Deep and Iterative Learning, Convolutional Neural Networks, and Language Change ... Beguš, Gašper. - : arXiv, 2020 BASE Show details 12 Modeling unsupervised phonetic and phonological learning in Generative Adversarial Phonology ... Beguš, Gašper. - : University of Mass Amherst, 2020 BASE Show details 13 CiwGAN and fiwGAN: Encoding information in acoustic data to model lexical learning with Generative Adversarial Networks ... Beguš, Gašper. - : arXiv, 2020 BASE Show details 14 Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks Beguš, Gašper In: Front Artif Intell (2020) BASE Show details 15 Modeling unsupervised phonetic and phonological learning in Generative Adversarial Phonology Beguš, Gašper In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 16 Unnatural Phonology: A Synchrony-Diachrony Interface Approach Beguš, Gašper. - 2018 Abstract: This dissertation addresses one of the most contested topics in phonology: which factors influence phonological typology and how to disambiguate these factors. I propose a new framework for modeling the influences of Analytic Bias and Channel Bias on phonological typology. The focus of the dissertation is unnatural alternations -- those that operate in precisely the opposite direction from some universal and articulatorily- or perceptually-motivated phonetic tendency. Based on a typological survey of unnatural alternations and gradient phonotactic restrictions, I propose a diachronic device for explaining unnatural processes called the Blurring Process and argue that minimally three sound changes are required for an unnatural segmental process to arise (Minimal Sound Change Requirement; MSCR). Based on the Blurring Process and MSCR, I propose a new model of deriving typology within Channel Bias. I introduce the concept of Historical Probabilities of Alternations (Pχ) and propose a method of estimating Historical Probabilities based on the statistical technique bootstrapping. The proposed framework has theoretical implications. The existence of unnatural gradient phonotactic restrictions reveals that both categorical Optimality Theory and weighted-constraint frameworks with restricted Con undergenerate. To address this shortcoming, I propose a formal model of phonological typology that combines estimates of Historical Probabilities with results from the artificial grammar learning experiments. The dissertation adopts the Maximum Entropy model and introduces prior Historical Weights (wχ), which are derived from the Historical Probabilities. Prior variance and Historical Weights allow for a disambiguation between Analytic and Channel Bias influences on typology: both metrics are compared to the observed typology, which yields a quantitative comparison between the two factors. To estimate the contribution of Analytic Bias, I conduct an artificial grammar learning experiment that tests learning of a complex and an unnatural alternation. By combining statistical modeling of diachronic developments with experimental work, the proposed framework allows controlling for Channel Bias factors when testing the Analytic Bias influences and vice-versa and, in turn, provides quantitative means for disambiguating Analytic and Channel Bias influences on typology. ; Linguistics ; phonology; typology; phonotactics; experimental phonology; historical linguistics; sound change; phonological learning; probabilistic models; naturalness; voice; Maximum entropy; complexity bias; bootstrapping Keyword: Language; Linguistics URL: http://nrs.harvard.edu/urn-3:HUL.InstRepos:40050094 BASE Hide details 17 Relativna kronologija naglasnih pojavov govora Žirovske kotline poljanskega narečja ; The Relative Chronology of Word-Prosodic Phenomena in the Local Dialect of the Žiri Basin (Poljana Dialect) Beguš, Gašper. - : ZRC SAZU and Hall Center for the Humanities, 2011 BASE Show details

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