Frequency Effect and Neutralization of Tones in Mandarin Chinese

IF 0.7 2区文学 0 LANGUAGE & LINGUISTICS

Journal of Quantitative Linguistics Pub Date : 2019-04-03 DOI:10.1080/09296174.2018.1452140

Huifang Kong, Shengyi Wu

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引用次数: 2

Abstract

Abstract Tonal neutralization in Mandarin has long been thought to be connected with lexical frequency. But this has never been investigated quantitatively because of the methodological challenge. In this study, a production experiment was run with speakers reading disyllabic words in neutral tones with frequency estimates derived from a Frequency Dictionary. The dependent measures were the three acoustic correlates of: duration, F0 contour and intensity. Independent measures included the lexical frequency at three levels (low, middle and high). Regression analysis showed that neutralization of tones are directly correlated with lexical frequency independent of other factors. A regularity, the more frequent, the shorter in duration; the more frequent, the lower in pitch; the more frequent, the weaker in intensity governs the neutralization of tones in reduced syllables. However, the exact shape of such an effect displays a different scenario in a different frequency range. Only high frequency words display a significant difference from low frequency words. Last but not the least, an exemplar representation is proposed to express a neutral tone’s observed frequency effect naturally.

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普通话声调的频率效应与中性化

摘要长期以来，人们一直认为普通话的音调中和与词汇频率有关。但由于方法上的挑战，这一点从未得到定量研究。在这项研究中，对说话者进行了一项生产实验，用频率词典中的频率估计值阅读中性音调的双音节单词。依赖性测量是三个声学相关性：持续时间、F0轮廓和强度。独立测量包括三个层次（低、中、高）的词汇频率。回归分析表明，声调的中和与词汇频率直接相关，不受其他因素的影响。规律性，频率越高，持续时间越短；频率越高，音调越低；越频繁，强度就越弱，这就决定了缩减音节中音调的中和。然而，这种效果的确切形状在不同的频率范围内显示了不同的场景。只有高频词显示出与低频词的显著差异。最后但并非最不重要的是，提出了一个示例表示来自然地表达中性音调的观察到的频率效应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Quantitative Linguistics Multiple-

CiteScore

2.90

自引率

7.10%

发文量

期刊介绍： The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.