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Phonetic effects of morphology and context: Modeling the duration of word-final S in English with naïve discriminative learning
Journal of Linguistics ( IF 1.381 ) Pub Date : 2019-06-14 , DOI: 10.1017/s0022226719000203
FABIAN TOMASCHEK , INGO PLAG , MIRJAM ERNESTUS , R. HARALD BAAYEN

Recent research on the acoustic realization of affixes has revealed differences between phonologically homophonous affixes, e.g. the different kinds of final [s] and [z] in English (Plag, Homann & Kunter 2017, Zimmermann 2016a). Such results are unexpected and unaccounted for in widely accepted post-Bloomfieldian item-and-arrangement models (Hockett 1954), which separate lexical and post-lexical phonology, and in models which interpret phonetic effects as consequences of different prosodic structure. This paper demonstrates that the differences in duration of English final S as a function of the morphological function it expresses (non-morphemic, plural, third person singular, genitive, genitive plural, cliticized has, and cliticized is) can be approximated by considering the support for these morphological functions from the words’ sublexical and collocational properties. We estimated this support using naïve discriminative learning and replicated previous results for English vowels (Tucker, Sims & Baayen 2019), indicating that segment duration is lengthened under higher functional certainty but shortened under functional uncertainty. We discuss the implications of these results, obtained with a wide learning network that eschews representations for morphemes and exponents, for models in theoretical morphology as well as for models of lexical processing.

中文翻译:

形态和上下文的语音效应:用朴素的判别学习模拟英语中词尾 S 的持续时间

最近对词缀的声学实现的研究揭示了语音同音词缀之间的差异,例如英语中不同类型的词尾 [s] 和 [z] (Plag, Homann & Kunter 2017, Zimmermann 2016a)。在广泛接受的后布卢姆菲尔德项目和排列模型(Hockett 1954)中,这种结果是出乎意料的,并且无法解释,该模型将词汇和后词汇音系分开,并且在将语音效果解释为不同韵律结构的结果的模型中。本文论证了英语词尾 S 的持续时间的差异作为它所表达的形态功能的函数(非词素、复数、第三人称单数、属格、复数属格、cliticized, 并被批评) 可以通过从单词的亚词汇和搭配属性考虑对这些形态功能的支持来近似。我们使用朴素的判别式学习估计了这种支持,并复制了以前对英语元音的结果(Tucker, Sims & Baayen 2019),表明在功能确定性较高的情况下片段持续时间会延长,但在功能不确定性的情况下会缩短。我们讨论了这些结果的含义,这些结果是通过广泛的学习网络获得的,该网络避开了词素和指数的表示、理论形态学模型以及词汇处理模型。
更新日期:2019-06-14
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