Doctoral Dissertation Research: Mechanisms of adaptive plasticity in speech perception

博士论文研究：言语感知的适应性可塑性机制

• 批准号: 1941357
• 负责人: Lori Holt
• 金额: $ 1.63万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941357&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Mechanisms; adaptive

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.

当谈话伙伴的讲话与预期不符时，理解就会受到影响。浓重的外国口音、独特的方言，甚至鼻塞都会影响语音的精确实现。人类的言语感知非常灵活。虽然理解最初受到影响时，讲话偏离规范的听众的语言社区，与曝光听众迅速适应和理解反弹。了解人类大脑能够灵活地将语音映射到语言表示的方法，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非常重要。本论文的研究项目将通过操纵短期语音输入来模拟口音，以确定语音感知如何适应。这提供了提供特定声学语音维度的加权如何作为短期语音输入变化（诸如口音）的函数而移位的详细测量的优点。该研究的中心假设是，语音交流中的自适应可塑性是由错误驱动的监督学习驱动的。研究1测试了由这一假设产生的预测：当短期语音输入偏离长期规范时，由语音类别激活支持的监督错误驱动学习将导致可用于语音识别的次级声学维度的权重降低。研究2通过实现算法理论的神经网络来实现这种预测。研究3探讨了在没有明显语音类别决策的自然听觉环境中，明显语音类别决策是否是适应性可塑性所必需的。最后，研究4使用头皮电生理检查的语言表征水平的影响，适应性可塑性在基于维度的统计学习。总而言之，该项目将进一步了解当语音输入偏离建立语言表征的长期规范时，语言系统如何应对沟通的挑战。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。