Doctoral Dissertation Research: Mechanisms of adaptive plasticity in speech perception

当谈话对象说出的话语与预期不符时，理解能力就会受到影响。浓重的外国口音，独特的方言，甚至鼻塞都会影响说话的准确实现。人类的语言感知非常灵活。虽然当言语偏离听者语言社区的规范时，理解能力最初会受到影响，但随着接触，听者会迅速适应并恢复理解能力。了解人类大脑能够灵活地将语音映射到语言表征的方法，对于推进下一代语言学理论、构建更强大的机器语音识别以及支持个人在非母语教育环境中进行交流具有重要意义。本论文的研究项目将通过操纵短期语音输入规律来模拟口音，以确定语音感知如何适应。这提供了一个详细测量特定声学语音维度的权重如何作为短期语音输入变化（如口音）的函数而变化的优势。初步研究支持了本研究的中心假设，即语音交流中的适应性可塑性是由错误驱动的监督学习驱动的。研究1验证了这一假设的一个预测：当短期语音输入规则偏离长期规范时，语音类别激活支持的监督式错误驱动学习将导致语音识别的次要声学维度权重下降。研究2通过实现算法理论的神经网络来实现这一预测。研究3探讨了在没有显性语音类别决定的自然主义听力环境中，显性语音类别决定是否对适应可塑性是必要的。最后，研究4利用头皮电生理学研究了基于维度的统计学习中适应可塑性对语言表征水平的影响。总而言之，该项目将促进对语言系统如何应对交流挑战的理解，当语音输入偏离建立语言表征的长期规范时。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Comprehension suffers when a conversation partner produces speech that departs from expectations. A strong foreign accent, a distinct dialect, or even a stuffy nose can impact the precise realization of speech. Human speech perception is remarkably flexible. Although comprehension initially suffers when speech departs from the norms of a listener’s language-community, with exposure listeners rapidly adapt and comprehension rebounds. Understanding the means by which the human brain is able to flexibly map speech to language representations promises to be important in advancing next generation linguistic theories, in building more robust machine speech recognition, and in supporting individuals communicating in non-native educational contexts. This dissertation research project will simulate an accent by manipulating short-term speech input regularities to determine how speech perception adapts. This offers the advantage of providing a detailed measure of how the weighting of specific acoustic speech dimensions shifts as a function of short-term speech input changes such as accent. The central hypothesis of the research, supported by preliminary studies, is that adaptive plasticity in speech communication is driven by error-driven supervised learning. Study 1 tests a prediction arising from this hypothesis: when short-term speech input regularities depart from long-term norms, supervised error-driven learning supported by phonetic category activation will result in down-weighting of secondary acoustic dimensions available for speech recognition. Study 2 pursues this prediction via a neural network that implements the algorithmic theory. Study 3 addresses whether overt phonetic category decisions are necessary for adaptive plasticity in a naturalistic listening environment without overt phonetic category decisions. Finally, Study 4 uses scalp electrophysiology to examine the level of linguistic representation impacted by adaptive plasticity in dimension-based statistical learning. In all, the project will advance understanding of how the linguistic system tackles the challenge of communication when speech input deviates from the long-term norms that established linguistic representations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.