词汇重音强度与大节奏强度:突出提示之间的反比关系

Christine Prechtel
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摘要

在韵律类型学的[1]模型中,短语-中间音调节奏或宏观节奏(MacR)是用于对语言进行分类的参数之一。如果一种语言的语调在一个单词大小的韵律单位中有规律地在高音调和低音调之间交替,那么这种语言就具有很强的MacR[1:52 22]。该模型预测,带有以上升或下降音调为标志的重音短语(AP)的语言比具有各种音高口音的语言具有更强的MacR。Jun观察到MacR弱的语言往往有较强的重音声学相关性,而MacR强的语言往往有较弱的重音声学相关性。因此,她预测了词汇重读强度与MacR强度之间的负相关关系。本研究通过比较英语、维吾尔语和孟加拉语的词汇重音和MacR相关性来验证这一预测。生产实验的结果发现英语的重音相关性最强,其次是维吾尔语,其次是孟加拉语,而MacR感知实验的结果发现了相反的排名(即孟加拉语b>维吾尔语b>英语)。这些初步结果表明,在突出提示和支持预测的反比关系之间存在潜在的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lexical Stress Strength vs Macro-Rhythm Strength: An Inverse Relationship Between Prominence Cues
In Jun’s [1] model of prosodic typology, phrase-medial tonal rhythm, or macro-rhythm (MacR), is one of the parameters used to classify languages. A language has strong MacR if its intonation regularly alternates between high and low tones over a word-sized prosodic unit in a phrase [1:522]. The model predicts that languages with an Accentual Phrase (AP) marked by a rising or falling tone will have stronger MacR than languages with various types of pitch accents. Jun observed that languages with weak MacR tend to have strong acoustic correlates of stress while languages with strong MacR tend to have weak acoustic correlates of stress. Therefore, she predicted an inverse correlation between the strength of lexical stress and the strength of MacR. The current study tests this prediction by comparing the lexical stress and MacR correlates of English, Uyghur, and Bengali. The results of a production experiment found that English had the strongest stress correlates, followed by Uyghur, followed by Bengali, and the results of a MacR perception experiment found some support for the opposite ranking (i.e., Bengali > Uyghur > English). These preliminary results suggest a potential trade-off between prominence cues and support for the predicted inverse relationship.
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