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1
Accessibility, Discoverability, and Functionality: An Audit of and Recommendations for Digital Language Archives
In: Journal of Open Humanities Data; Vol 8 (2022); 10 ; 2059-481X (2022)
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2
Acoustic correlates of stress in 16 Australian languages [R Markdown] ...
Babinski, Sarah. - : Zenodo, 2022
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3
Arlequin input file for phylogenetic analysis [ARP file] ...
Babinski, Sarah. - : Zenodo, 2022
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4
Arlequin input file for phylogenetic analysis [ARP file] ...
Babinski, Sarah. - : Zenodo, 2022
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5
Acoustic correlates of stress in 16 Australian languages [R Markdown] ...
Babinski, Sarah. - : Zenodo, 2022
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6
Automatic categorization of prosodic contours in Bardi
In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5218 ; 2473-8689 (2022)
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7
How usable are digital collections for endangered languages? A review
In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5219 ; 2473-8689 (2022)
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8
Intrinsic f0 and Sound Change: Evidence from Australian Languages
In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2020 Annual Meeting on Phonology ; 2377-3324 (2021)
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9
Gender representation in linguistic example sentences ...
Kotek, Hadas; Rikker Dockum; Babinski, Sarah. - : Unpublished, 2020
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10
Gender representation in linguistic example sentences
In: Proceedings of the Linguistic Society of America; Vol 5, No 1 (2020): Proceedings of the Linguistic Society of America; 514–528 ; 2473-8689 (2020)
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11
A Robin Hood approach to forced alignment: English-trained algorithms and their use on Australian languages
In: Proceedings of the Linguistic Society of America; Vol 4 (2019): Proceedings of the Linguistic Society of America; 3:1–12 ; 2473-8689 (2019)
Abstract: Forced alignment automatically aligns audio recordings of spoken language with transcripts at the segment level, greatly reducing the time required to prepare data for phonetic analysis. However, existing algorithms are mostly trained on a few well-documented languages. We test the performance of three algorithms against manually aligned data. For at least some tasks, unsupervised alignment (either based on English or trained from a small corpus) is sufficiently reliable for it to be used on legacy data for low-resource languages. Descriptive phonetic work on vowel inventories and prosody can be accurately captured by automatic alignment with minimal training data. Consonants provided significantly more challenges for forced alignment.
Keyword: Australian languages; forced alignment; Language Documentation; Phonetics; Yidiny
URL: http://journals.linguisticsociety.org/proceedings/index.php/PLSA/article/view/4468
https://doi.org/10.3765/plsa.v4i1.4468
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