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SCALa: A blueprint for computational models of language acquisition in social context
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In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.inria.fr/hal-03373586 ; Cognition, Elsevier, 2021, 213, pp.104779. ⟨10.1016/j.cognition.2021.104779⟩ (2021)
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Seshat: A tool for managing and verifying annotation campaigns of audio data
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02496041 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.6976-6982 (2020)
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An open-source voice type classifier for child-centered daylong recordings
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In: Interspeech 2020 - Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02989487 ; Interspeech 2020 - Conference of the International Speech Communication Association, Oct 2020, Shanghai / Virtual, China (2020)
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Speaker detection in the wild: Lessons learned from JSALT 2019
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In: Odyssey 2020 The Speaker and Language Recognition Workshop ; https://hal.archives-ouvertes.fr/hal-02417632 ; Odyssey 2020 The Speaker and Language Recognition Workshop, Nov 2020, Tokyo, Japan (2020)
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Longform recordings : Opportunities and challenges ; Enregistrements de longue durée: Opportunités et défis
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In: Actes des 2èmes journées scientifiques du Groupement de Recherche Linguistique Informatique Formelle et de Terrain (LIFT). ; LIFT 2020 - 2èmes journées scientifiques du Groupement de Recherche "Linguistique informatique, formelle et de terrain" ; https://hal.archives-ouvertes.fr/hal-03047153 ; LIFT 2020 - 2èmes journées scientifiques du Groupement de Recherche "Linguistique informatique, formelle et de terrain", Dec 2020, Montrouge / Virtual, France. pp.64-71 (2020)
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Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019)
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WordSeg: Standardizing unsupervised word form segmentation from text
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Bernard, Mathieu; Thiollière, Roland; Saksida, Amanda; Loukatou, Georgia; Larsen, Elin; Johnson, Mark,; Fibla, Laia; Dupoux, Emmanuel; Daland, Robert; Cao, Xuan-Nga; Cristia, Alejandrina
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In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-02274072 ; Behavior Research Methods, Psychonomic Society, Inc, 2019, ⟨10.3758/s13428-019-01223-3⟩ (2019)
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Abstract:
International audience ; A basic task in first language acquisition likely involves discovering the boundaries between words or morphemes in input where these basic units are not overtly segmented. A number of unsupervised learning algorithms have been proposed in the last 20 years for these purposes, some of which have been implemented computationally, but whose results remain difficult to compare across papers. We created a tool that is open source, enables reproducible results, and encourages cumulative science in this domain. WordSeg has a modular architecture: It combines a set of corpora description routines, multiple algorithms varying in complexity and cognitive assumptions (including several that were not publicly available, or insufficiently documented), and a rich evaluation package. In the paper, we illustrate the use of this package by analyzing a corpus of child-directed speech in various ways, which further allows us to make recommendations for experimental design of follow-up work. Supplementary materials allow readers to reproduce every result in this paper, and detailed online instructions further enable them to go beyond what we have done. Moreover, the system can be installed within container software that ensures a stable and reliable environment. Finally, by virtue of its modular architecture and transparency, WordSeg can work as an open-source platform, to which other researchers can add their own segmentation algorithms.
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Keyword:
[SCCO.LING]Cognitive science/Linguistics; Cumulative science; First language acquisition; Natural language processing; Unsupervised word discovery
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URL: https://doi.org/10.3758/s13428-019-01223-3
https://hal.archives-ouvertes.fr/hal-02274072
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Are Words Easier to Learn From Infant- Than Adult-Directed Speech? A Quantitative Corpus-Based Investigation
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In: ISSN: 0364-0213 ; EISSN: 1551-6709 ; Cognitive Science ; https://hal.archives-ouvertes.fr/hal-01888701 ; Cognitive Science, Wiley, 2018, 42 (5), pp.1586 - 1617. ⟨10.1111/cogs.12616⟩ (2018)
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Child-Directed Speech Is Infrequent in a Forager-Farmer Population: A Time Allocation Study
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In: ISSN: 0009-3920 ; EISSN: 1467-8624 ; Child Development ; https://hal.inria.fr/hal-01687336 ; Child Development, Wiley, 2017, ⟨10.1111/cdev.12974⟩ (2017)
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Relating Unsupervised Word Segmentation to Reported Vocabulary Acquisition
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In: Interspeech 2017 ; https://hal.inria.fr/hal-01687534 ; Interspeech 2017, 2017, Stockholm, Sweden. ⟨10.21437/Interspeech.2017-937⟩ (2017)
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The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective
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In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) ; https://hal.inria.fr/hal-01687451 ; Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Jul 2017, Vancouver, Canada. ⟨10.18653/v1/P17-2028⟩ (2017)
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The more, the better? Behavioral and neural correlates of frequent and infrequent vowel exposure
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In: ISSN: 0012-1630 ; EISSN: 1098-2302 ; Developmental Psychobiology ; https://hal.inria.fr/hal-01687403 ; Developmental Psychobiology, Wiley, 2017, 59 (5), pp.603 - 612. ⟨10.1002/dev.21534⟩ (2017)
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