Tupper Paul, Leung Keith, Wang Yue, Jongman Allard, Sereno Joan A
Department of Mathematics, Simon Fraser University, Burnaby, British Colombia V5A 1S6, Canada.
Department of Linguistics, Simon Fraser University, Burnaby, British Colombia V5A 1S6, Canada.
J Acoust Soc Am. 2020 Apr;147(4):2570. doi: 10.1121/10.0001024.
This study aims to characterize distinctive acoustic features of Mandarin tones based on a corpus of 1025 monosyllabic words produced by 21 native Mandarin speakers. For each tone, 22 acoustic cues were extracted. Besides standard F0, duration, and intensity measures, further cues were determined by fitting two mathematical functions to the pitch contours. The first function is a parabola, which gives three parameters: a mean F0, an F0 slope, and an F0 second derivative. The second is a broken-line function, which models the contour as a continuous curve consisting of two lines with a single breakpoint. Cohen's d, sparse Principal Component Analysis, and other statistical measures are used to identify which of the cues, and which combinations of the cues, are important for distinguishing each tone from each other among all the speakers. Although the specific cues that best characterize the tone contours depend on the particular tone and the statistical measure used, this paper shows that the three cues obtained by fitting a parabola to the tone contour are broadly effective. This research suggests using these three cues as a canonical choice for defining tone characteristics.
本研究旨在基于21名以普通话为母语者所产出的1025个单音节词的语料库，刻画普通话声调独特的声学特征。对于每个声调，提取了22个声学线索。除了标准的基频、时长和强度测量外，还通过将两个数学函数拟合到音高轮廓上来确定进一步的线索。第一个函数是抛物线，它给出三个参数：平均基频、基频斜率和基频二阶导数。第二个是折线函数，它将轮廓建模为一条由两条线和一个断点组成的连续曲线。使用科恩d值、稀疏主成分分析和其他统计方法来确定哪些线索以及线索的哪些组合对于区分所有说话者之间的每个声调最为重要。尽管最能刻画声调轮廓的具体线索取决于特定的声调以及所使用的统计方法，但本文表明通过将抛物线拟合到声调轮廓上获得的这三个线索具有广泛的有效性。本研究建议将这三个线索作为定义声调特征的标准选择。
