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.

当对话伙伴发出的言语与预期不同时，理解力就会受到影响。尽管当言语偏离预期时，理解力最初会受到影响。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生成理论、建立更强大的语言理论非常重要。该论文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。特定声学语音维度的权重随着短期语音输入变化（例如口音）而变化。得到初步研究支持的该研究的中心假设是，语音通信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 检验由此产生的预测假设：当短期语音输入规律偏离长期规范时，由语音类别激活支持的监督误差驱动学习将导致可用于语音识别的次要声学维度的权重降低，研究 2 通过神经网络进行这一预测。研究 3 讨论了在没有明显语音类别决策的自然聆听环境中，明显的语音类别决策对于自适应可塑性是否是必要的。最后，研究 4 使用头皮电生理学来检查自适应可塑性影响的语言表征水平。总而言之，该项目将促进人们对当语音输入偏离建立语言表示的长期规范时语言系统如何应对沟通挑战的理解。该奖项是 NSF 的法定使命，并被认为是值得的。通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。