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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) Abstract: This paper models phonetic and phonological learning as a dependency between random space and generated speech data in the Generative Adversarial Neural network architecture and proposes a methodology to uncover the network’s internal representation that corresponds to phonetic and phonological features. A Generative Adversarial Network (Goodfellow et al. 2014; implemented as WaveGAN for acoustic data by Donahue et al. 2019) was trained on an allophonic distribution in English, where voiceless stops surface as aspirated word-initially before stressed vowels except if preceded by a sibilant [s]. The network successfully learns the allophonic alternation: the network’s generated speech signal contains the conditional distribution of aspiration duration. Additionally, the network generates innovative outputs for which no evidence is available in the training data, suggesting that the network segments continuous speech signal into units that can be productively recombined. The paper also proposes a technique for establishing the network’s internal representations. We identify latent variables that directly correspond to presence of [s] in the output. By manipulating these variables, we actively control the presence of [s], its frication amplitude, and spectral shape of the frication noise in the generated outputs. Keyword: allophonic distribution; artificial intelligence; Computational Linguistics; generative adversarial networks; language acquisition; neural networks; phonetic learning; phonological learning; speech; voice onset time URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1129&context=scil https://scholarworks.umass.edu/scil/vol3/iss1/15 BASE Hide details 16 Unnatural Phonology: A Synchrony-Diachrony Interface Approach Beguš, Gašper. - 2018 BASE Show 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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