Guenther F H
Department of Cognitive and Neural Systems, Boston University, Massachusetts 02215, USA.
Psychol Rev. 1995 Jul;102(3):594-621. doi: 10.1037/0033-295x.102.3.594.
This article describes a neural network model of speech motor skill acquisition and speech production that explains a wide range of data on variability, motor equivalence, coarticulation, and rate effects. Model parameters are learned during a babbling phase. To explain how infants learn language-specific variability limits, speech sound targets take the form of convex regions, rather than points, in orosensory coordinates. Reducing target size for better accuracy during slower speech leads to differential effects for vowels and consonants, as seen in experiments previously used as evidence for separate control processes for the 2 sound types. Anticipatory coarticulation arises when targets are reduced in size on the basis of context; this generalizes the well-known look-ahead model of coarticulation. Computer simulations verify the model's properties.
本文描述了一种语音运动技能习得和语音产生的神经网络模型，该模型解释了关于变异性、运动等效性、协同发音和速率效应的大量数据。模型参数在咿呀学语阶段学习。为了解释婴儿如何学习特定语言的变异性限制，语音目标在口部感觉坐标中采用凸区域而非点的形式。在较慢语速时减小目标大小以提高准确性会导致元音和辅音产生不同的效果，这与之前用作两种声音类型存在单独控制过程证据的实验结果一致。当根据语境减小目标大小时会出现预期协同发音；这推广了著名的协同发音前瞻模型。计算机模拟验证了该模型的特性。
