Collaborative Research: A Bayesian model of phonetic and phonotactic effects in cross-language speech production

人类认知的一个基本方面是感知和产生语言的能力。对非母语语言处理的研究提供了一些与这种能力有关的最引人注目的证据：当人类试图感知或产生包含外来声音或声音序列的单词时，他们表现出正确和不正确的系统模式。先前的研究表明，不同的非原生结构会导致不同的错误率和类型；据推测，这些差异可以用语法、感知和发音因素的组合来解释。该项目的主要目标是为这些因素提供仔细控制的实验评估，并开发一个明确的概率模型，说明它们如何在人类表现中相互作用。需要研究的具体实验问题有：(1)人类在处理非母语语音时最敏感的语音特征是什么；(2)输入和环境声学的质量如何影响非本地感知和生产；(3)学习词义是否可以调节对非母语语音细节特性的敏感性。计算模型建立在越来越多的工作基础上，这些工作表明，人类的感知和行为反映了基于先前预期和嘈杂的感官测量的最佳贝叶斯推断。相关的先验反映了母语的知识；该模型预测，与母语结构更相似的非母语结构应该被处理得更准确。该模型还预测，具有强大感知特性的非母语声音应该被更准确地处理，即使它们的先验概率很低。该模型的发展将提供给其他研究人员，将促进音韵学和语音学在认知科学研究的更广泛背景下的作用。由于我们的实验考察了课堂声学和说话者变化对非母语声音处理的影响，因此本项目也对外语教学产生了影响。

A fundamental aspect of human cognition is the capacity to perceive and produce language. Studies of non-native speech processing provide some of the most striking evidence bearing on this capacity: when humans attempt to perceive or produce words containing foreign sounds or sound sequences, they show systematic patterns of correct and incorrect performance. Prior research has established that different non-native structures elicit different rates and types of error; it has been hypothesized that these differences can be explained by a combination of grammatical, perceptual, and articulatory factors. The main goals of this project are to provide carefully controlled experimental evaluations of these factors, and to develop an explicit, probabilistic model of how they interact in human performance. Particular experimental issues to be investigated are: (1) what phonetic characteristics humans are most sensitive to when processing non-native sounds; (2) how the quality of the input and ambient acoustics affect non-native perception and production; and (3) whether learning word meaning can modulate sensitivity to detailed properties of non-native sounds. The computational model builds on a growing body of work suggesting that human perception and action reflect optimal Bayesian inference conditioned on prior expectations and noisy sensory measurements. The relevant prior reflects knowledge of the native language; the model predicts that non-native structures that are more similar to those in the native language should be processed with greater accuracy. The model also predicts that non-native sounds with robust perceptual properties should be processed more accurately, even if their prior probabilities are low. The development of this model, which will be made available to other researchers, will promote the role of phonology and phonetics within the broader context of cognitive science research. Because our experiments examine the impact of classroom acoustics and talker variation on non-native sound processing, this project also has ramifications for foreign language pedagogy.