Department of Linguistics, University of Alberta, Edmonton, Alberta T6G 2R3, Canada.
Department of English, George Mason University, Fairfax, Virginia 22030, USA.
J Acoust Soc Am. 2024 Jan 1;155(1):294-305. doi: 10.1121/10.0024345.
This study constitutes an investigation into the acoustic variability of intervocalic alveolar taps in a corpus of spontaneous speech from Madrid, Spain. Substantial variability was documented in this segment, with highly reduced variants constituting roughly half of all tokens during spectrographic inspection. In addition to qualitative documentation, the intensity difference between the tap and surrounding vowels was measured. Changes in this intensity difference were statistically modeled using Bayesian finite mixture models containing lexical and phonetic predictors. Model comparisons indicate predictive performance is improved when we assume two latent categories, interpreted as two pronunciation variants for the Spanish tap. In interpreting the model, predictors were more often related to categorical changes in which pronunciation variant was produced than to gradient intensity changes within each tap type. Variability in tap production was found according to lexical frequency, speech rate, and phonetic environment. These results underscore the importance of evaluating model fit to the data as well as what researchers modeling phonetic variability can gain in moving past linear models when they do not adequately fit the observed data.
这项研究调查了西班牙马德里自然语料库中元音间 alveolar tap 的声学变异性。该音段表现出了高度的可变性，在声谱检查中，高度简化的变体大约占所有音位的一半。除了定性记录外，还测量了 tap 和周围元音之间的强度差异。使用包含词汇和语音预测器的贝叶斯有限混合模型对该强度差异进行了统计建模。模型比较表明，当我们假设存在两个潜在类别时，即假设西班牙语 tap 有两种发音变体，模型的预测性能会得到提高。在解释模型时，预测器更常与产生哪种发音变体的类别变化有关，而不是与每个 tap 类型内的强度渐变变化有关。tap 的产生存在词汇频率、语速和语音环境方面的差异。这些结果强调了评估模型对数据的拟合程度的重要性，以及当线性模型不能充分拟合观察到的数据时，研究语音变异性的人员可以通过超越线性模型获得什么。
