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1
Universal Dependencies and Semantics for English and Hebrew Child-directed Speech
In: Proceedings of the Society for Computation in Linguistics (2022)
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Do Infants Really Learn Phonetic Categories?
In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-03550830 ; Open Mind, MIT Press, 2021, 5, pp.113-131. ⟨10.1162/opmi_a_00046⟩ (2021)
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Early phonetic learning without phonetic categories -- Insights from large-scale simulations on realistic input
In: ISSN: 0027-8424 ; EISSN: 1091-6490 ; Proceedings of the National Academy of Sciences of the United States of America ; https://hal.archives-ouvertes.fr/hal-03070566 ; Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2021, 118 (7), pp.e2001844118. ⟨10.1073/pnas.2001844118⟩ (2021)
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Black or White but never neutral: How readers perceive identity from yellow or skin-toned emoji ...
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5
A phonetic model of non-native spoken word processing ...
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Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech ...
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7
Do Infants Really Learn Phonetic Categories?
In: Open Mind (Camb) (2021)
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8
Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input
In: Proc Natl Acad Sci U S A (2021)
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9
Multilingual acoustic word embedding models for processing zero-resource languages ...
Abstract: Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more ... : 5 pages, 4 figures, 1 table; accepted to ICASSP 2020. arXiv admin note: text overlap with arXiv:1811.00403 ...
Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering
URL: https://dx.doi.org/10.48550/arxiv.2002.02109
https://arxiv.org/abs/2002.02109
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10
Improved acoustic word embeddings for zero-resource languages using multilingual transfer ...
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11
Analyzing autoencoder-based acoustic word embeddings ...
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12
Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals ...
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13
Evaluating computational models of infant phonetic learning across languages ...
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14
Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages
In: http://infoscience.epfl.ch/record/277105 (2020)
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15
On understanding character-level models for representing morphology
Vania, Clara. - : The University of Edinburgh, 2020
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16
Methods for morphology learning in low(er)-resource scenarios
Bergmanis, Toms. - : The University of Edinburgh, 2020
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17
Discovering and analysing lexical variation in social media text
Shoemark, Philippa Jane. - : The University of Edinburgh, 2020
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18
Are we there yet? Encoder-decoder neural networks as cognitive models of English past tense inflection ...
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19
Analyzing ASR pretraining for low-resource speech-to-text translation ...
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20
Low-resource speech translation
Bansal, Sameer. - : The University of Edinburgh, 2019
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