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Boundary-conditioned anticipatory tonal coarticulation in Standard Mandarin
Journal of Phonetics ( IF 1.9 ) Pub Date : 2021-01-09 , DOI: 10.1016/j.wocn.2020.101018
Yan Sun , Chilin Shih

The goal of this study is to examine (1) whether and how the F0 contour of a sequence of neutral tones in Standard Mandarin is affected by the following full tone due to anticipatory tonal coarticulation, (2) whether and how the degree of anticipatory tonal coarticulation varies as a function of the strength of an intervening boundary, and (3) whether boundary-conditioned anticipatory tonal coarticulatory variations can be accounted for by boundary-induced durational changes. By using generalized additive mixed models to analyze time-varying F0 contours of neutral-tone sequence, we are able to formally detect anticipatory tonal coarticulation effects across different boundary conditions while simultaneously taking into account individual variations of 20 speakers. Results show that (a) F0 contours of neutral-tone sequence can be affected by both assimilatory and dissimilatory anticipatory effects and anticipation could spread to non-adjacent neutral tones, that (b) anticipatory tonal coarticulation becomes weaker when crossing a strong boundary than when crossing a weak boundary, and that (c) boundary-induced anticipatory tonal coarticulation reduction cannot be simply accounted for by boundary-induced lengthening effect. Based on the results, we argue that the anticipatory tonal coarticulation patterns reported in this study are more consistent with the soft template mark-up language (Stem-ML) model than with the parallel encoding and target approximation (PENTA) model, and that boundary-induced anticipatory tonal coarticulation reduction is primarily the result of active speech planning.



中文翻译:

标准普通话的边界条件预期音调发音

这项研究的目的是研究(1)由于预期音调的共鸣,标准普通话中性音调序列的F0轮廓是否受到以下全音的影响,以及如何受到其影响;(2)预期音调的程度和程度如何关节发音随介入边界强度的变化而变化,(3)边界引起的持续音调变化是否可以解释边界条件的预期音调发音变化。通过使用广义加性混合模型来分析中性音序的时变F0轮廓,我们能够正式检测跨不同边界条件的预期音调共音效果,同时考虑20个说话者的个体差异。不能简单地通过边界引起的拉长效应来解释。根据结果​​,我们认为本研究中报告的预期音调共音模式与软模板标记语言(Stem-ML)模型相比,与并行编码和目标近似(PENTA)模型以及该边界更一致引起的预期音调共音减少主要是主动语音计划的结果。

更新日期:2021-01-10
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