Assmann P F
School of Human Development, University of Texas at Dallas 75083, USA.
J Acoust Soc Am. 1996 Aug;100(2 Pt 1):1141-52. doi: 10.1121/1.416299.
When two synthetic vowels are presented concurrently and monaurally, listeners identify the members of the pair more accurately if they differ in fundamental frequency (F0), or if one of them is preceded or followed by formant transitions that specify a glide or liquid consonant. However, formant transitions do not help listeners identify the vowel to which they are linked; instead, they make the competing vowel easier to identify. One explanation is that the formant transition region provides a brief time interval during which the competing vowel is perceptually more prominent. This interpretation is supported by the predictions of two computational models of the identification of concurrent vowels that (i) perform a frequency analysis using a bank of bandpass filters, (ii) analyze the waveform in each channel using a brief, sliding temporal window, and (iii) determine which region of the signal provides the strongest evidence of each vowel. Model A [Culling and Darwin, J. Acoust. Soc. Am. 95, 1559-1569 (1994)] computes the rms energy in each channel at successive time intervals to generate running excitation patterns that serve as input to a vowel classifier, implemented as a linear associative neural network. Model B uses a temporal analysis in each channel to generate running autocorrelation functions, and it includes a further stage of source segregation [Meddis and Hewitt, J. Acoust. Soc. Am. 91, 233-245 (1992)] to partition the channels into two groups, one group providing evidence of the periodicity of the vowel with the dominant F0, the other group providing evidence of the competing vowel. Both models predicted effects of F0 and formant transitions on identification, but model B provided more accurate predictions of the pattern of listeners' identification responses. Taken together, the empirical and modeling results support the idea that the identification of concurrent vowels involves an analysis of the composite waveform using a sliding temporal window, combined with a form of F0-guided source segregation.
当两个合成元音同时单耳呈现时，如果它们的基频（F0）不同，或者其中一个元音之前或之后有指定滑音或流音辅音的共振峰过渡，听众就能更准确地识别这对元音中的成员。然而，共振峰过渡并不能帮助听众识别与之相连的元音；相反，它们会使竞争元音更容易被识别。一种解释是，共振峰过渡区域提供了一个短暂的时间间隔，在此期间竞争元音在感知上更加突出。这一解释得到了两个同时呈现元音识别计算模型预测的支持，这两个模型：（i）使用一组带通滤波器进行频率分析；（ii）使用一个短暂的滑动时间窗口分析每个通道中的波形；（iii）确定信号的哪个区域为每个元音提供了最有力的证据。模型A [Culling和Darwin，《美国声学学会杂志》95, 1559 - 1569 (1994)] 在连续的时间间隔内计算每个通道的均方根能量，以生成运行激励模式，作为元音分类器的输入，该元音分类器实现为一个线性联想神经网络。模型B在每个通道中进行时间分析以生成运行自相关函数，并且它包括一个进一步的声源分离阶段 [Meddis和Hewitt，《美国声学学会杂志》91, 233 - 245 (1992)]，将通道分为两组，一组提供具有主导F0的元音周期性的证据，另一组提供竞争元音的证据。两个模型都预测了F0和共振峰过渡对识别的影响，但模型B对听众识别反应模式的预测更准确。综合来看，实证和建模结果支持这样一种观点，即同时呈现元音的识别涉及使用滑动时间窗口对复合波形进行分析，并结合一种F0引导的声源分离形式。
