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Modeling monophthongal versus diphthongal /aɪ/ in sung vocal performance with interpretable machine learning.

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Title: Modeling monophthongal versus diphthongal /aɪ/ in sung vocal performance with interpretable machine learning.
Authors: De Timmerman R; Department of Linguistics, Ghent University, Ghent 9000, Belgium., Verbeke G; Department of Linguistics, Ghent University, Ghent 9000, Belgium.
Source: The Journal of the Acoustical Society of America [J Acoust Soc Am] 2026 Jul 01; Vol. 160 (1), pp. 537-549.
Publication Type: Journal Article
Journal Info: Publisher: American Institute of Physics Country of Publication: United States NLM ID: 7503051 Publication Model: Print Cited Medium: Internet ISSN: 1520-8524 (Electronic) Linking ISSN: 00014966 NLM ISO Abbreviation: J Acoust Soc Am Subsets: MEDLINE
Database: MEDLINE Ultimate
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        Value: 10.1121/10.0044380
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      – Code: eng
        Text: English
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        StartPage: 537
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      – TitleFull: Modeling monophthongal versus diphthongal /aɪ/ in sung vocal performance with interpretable machine learning.
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            NameFull: De Timmerman R
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            – D: 01
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              Text: 2026 Jul 01
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              Y: 2026
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              Value: 160
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