Doctoral Dissertation Research: Lexical Acoustics

博士论文研究：词汇声学

• 批准号: 1918404
• 负责人: Allard Jongman
• 金额: $ 0.99万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918404&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Lexical; Acoustics

This project will document which acoustic properties of the speech signal English listeners use to communicate by looking at patterns in both their correct recognition of words, and their confusions between words, when listening in a noisy environment. The project will enhance understanding of speech production and perception, particularly regarding how acoustic features are distributed throughout the words of a language to minimize misperceptions and therefore facilitate reliable communication. More broadly, this work is relevant for research on the relation between speech sounds and word knowledge in both adults and children, contributing to the identification of unique distinctions between languages that affect second language learners, and ultimately informing models focusing on various types of hearing impairment in everyday communication.Two general models of the structure of the sound system of a language are contrasted. The first model, referred to as the "inventory model", is the canonical approach that forms the basis for most phonetic descriptions of sound systems in speech perception. Under the inventory model, the sound system is structured as a fixed set of contrastive consonants and vowels, where this set is treated as homogeneous in weight such that all elements play an equal role in defining the system and determining the relative importance of individual acoustic cues. The second model, referred to as the lexicon model, considers the system of contrastive sounds to be critically distributed over the lexicon, such that the relative weight of different cues now depends on the number and configuration of words those cues serve to distinguish. In contrasting these two approaches, two main questions are asked: (1) how well do the models agree in their estimates of cue weights, and what accounts for points of agreement and disagreement; and (2) what does each model entail for the stability of the system in the presence of background noise in the environment. The inventory model uses a database of controlled productions of syllables to predict prior-published confusion patterns between sounds, with cue weights then measured as the relative importance of each cue in the model predicting listener perception patterns. For the lexicon model, a database of nearly 27,000 words produced by a single speaker is used to predict listener word recognition patterns when these words are embedded in noise in a series of six experiments utilizing different tasks (open, fill-in-the-blank recognition, and closed choice between two similar-sounding words) and different stimuli (naturally produced, enhanced, and degraded). The cue weights from these two model fits are then compared to address the first question. To answer the second question, simulations of noise added to different cues, and the resulting change in predicted similarity between sounds (in the inventory case) and words (in the lexicon case), are used to study the stability and adaptive response of each system to uncertainty in the acoustic signal. As such, these experiments will be able to address key claims about the structure and organization of sound systems in language.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.

这个项目将记录英语听众在嘈杂的环境中收听时，通过观察他们正确识别单词和单词之间混淆的模式来交流语音信号的声学特性。该项目将加强对语音产生和感知的理解，特别是关于声学特征如何分布在一种语言的单词中，以尽量减少误解，从而促进可靠的沟通。更广泛地说，这项工作是相关的语音和文字知识之间的关系在成人和儿童的研究，有助于识别语言之间的独特区别，影响第二语言学习者，并最终通知模型集中在日常交流中的各种类型的听力障碍。两个一般模型的语音系统的结构的语言进行了对比。第一个模型，称为“库存模型”，是规范方法，构成了语音感知中声音系统的大多数语音描述的基础。在库存模型下，声音系统被构造为一组固定的对比辅音和元音，其中该集合被视为权重均匀的，以便所有元素在定义系统和确定单个声学线索的相对重要性方面发挥同等作用。第二个模型，被称为词典模型，认为系统的对比声音是严格分布在词典，这样不同的线索的相对权重现在取决于这些线索用于区分的单词的数量和配置。在对比这两种方法时，提出了两个主要问题：（1）模型在估计线索权重时的一致性如何，以及一致点和不一致点的原因是什么;（2）在环境中存在背景噪声的情况下，每个模型对系统的稳定性有什么影响。该库存模型使用一个数据库的控制生产的音节，以预测先前公布的声音之间的混淆模式，与线索的权重，然后测量为相对重要性的模型中的每个线索预测听众的感知模式。对于词典模型，由单个说话者产生的近27，000个单词的数据库用于预测听者单词识别模式，当这些单词嵌入噪声中时，在一系列的六个实验中，利用不同的任务（开放，填空识别，以及在两个发音相似的单词之间进行封闭选择）和不同的刺激（自然产生，增强和退化）。然后比较来自这两个模型拟合的线索权重以解决第一个问题。为了回答第二个问题，模拟的噪声添加到不同的线索，以及由此产生的变化，预测的相似性之间的声音（在库存的情况下）和单词（在词典的情况下），被用来研究的稳定性和自适应响应的每个系统的不确定性的声学信号。因此，这些实验将能够解决有关语言中声音系统的结构和组织的关键主张。该奖项反映了NSF的法定使命，并被认为值得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来支持。