Unmasking the acoustic effects of vowel-to-vowel coarticulation: A statistical modeling approach
Unmasking the acoustic effects of vowel-to-vowel coarticulation: A statistical modeling approach
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DOI：
10.1016/j.wocn.2009.08.004
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发表时间：
2010-04-01
影响因子：
1.9
通讯作者：
McMurray, Bob
中科院分区：
文献类型：
--
作者：
Cole, Jennifer;Linebaugh, Gary;McMurray, Bob
协同发音是元音声学变异性的一个来源，但相对于其他变异性来源，这种影响有多大？我们研究了预期v - v协同发音相对于由于以下C和单个说话者的变化的声学效应。我们研究了10位美式英语使用者在48个V1- c# V2语境中V1中的F1和F2。方差分析显示V2和C对VI的F I和F2测量有显著影响。使用层次线性回归评估V2和C对相对于说话人和目标元音同一性的声学变异性的影响。说话者和目标元音约占F1和F2总变异的80%，但当这种变异被分割出去时，C和V2又占剩余目标元音变异的18% （F1）和63% （F2）。建立了多项逻辑回归（MLR）模型来检验目标元音F1和F2对即将到来的上下文的C和V2的预测能力。C-Place的预测准确率为58%，C-Voicing为76%，V2为54%，但只有在排除了其他来源的方差后才会如此。本文将MLR作为语音感知分析机制的一种模型进行了讨论。2009爱思唯尔有限公司版权所有。
Coarticulation is a source of acoustic variability for vowels, but how large is this effect relative to other sources of variance? We investigate acoustic effects of anticipatory V-to-V coarticulation relative to variation due to the following C and individual speaker. We examine F1 and F2 from V1 in 48 V1-C#V2 contexts produced by 10 speakers of American English. ANOVA reveals significant effects of both V2 and C on F I and F2 measures of VI. The influence of V2 and C on acoustic variability relative to that of speaker and target vowel identity is evaluated using hierarchical linear regression. Speaker and target vowel account for roughly 80% of the total variance in F1 and F2, but when this variance is partialed out C and V2 account for another 18% (F1) and 63% (F2) of the remaining target vowel variability. Multinomial logistic regression (MLR) models are constructed to test the power of target vowel F1 and F2 for predicting C and V2 of the upcoming context. Prediction accuracy is 58% for C-Place, 76% for C-Voicing and 54% for V2, but only when variance due to other sources is factored out. MLR is discussed as a model of the parsing mechanism in speech perception. (C) 2009 Elsevier Ltd. All rights reserved.