Time Course of Spoken Word Recognition

口语单词识别的时间进程

• 批准号: 8507782
• 负责人: Michael Kehas Tanenhaus
• 金额: $ 24.08万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8507782
• 关键词: Accent; Access to Information; Accounting; Acoustics; Address; Adult; Affect; Aphasia; Cereals; Characteristics; Comprehension; Cues; Data; Dependency; Disease; Elderly; Environment; Event; Exposure to; Eye; Eye Movements; General Population; Goals; Infant; Injury; Knowledge; Laboratories; Language; Language Development; Link; Longevity; Mediating; Methods; Modeling; Participant; Pattern; Perceptual learning; Phonetics; Play; Population; Process; Relative (related person); Research; Role; Saccades; Schools; Semantics; Series; Signal Transduction; Speech; Speech Sound; Stimulus; Stroke; Structure; System; Time; Visual; Weight; Word Processing; Work; base; computerized data processing; contextual factors; design; deviant; expectation; hearing impairment; information display; language processing; lexical; lexical processing; phonology; programs; research study; sample fixation; success; syntax; tool

DESCRIPTION (provided by applicant): Adults can recognize between 50,000 and 100,000 words, spoken by different talkers in varying acoustic environments. This ability is remarkable because speech unfolds as a series of transient acoustic events extending over a few hundred ms, without reliable cues to word boundaries. Despite the ubiquitous success of spoken language processing among the general population, 5 percent of first graders enter school with some type of speech sound disorder that cannot be accounted for by hearing impairment. In addition, once the language system has been successfully acquired, it is susceptible to insult from injury or stroke (accounting for 1 million adults in the U.S. with some form of aphasia). Spoken word recognition plays a central role in language acquisition and spoken language comprehension, allowing for storage of the rich array of syntactic, semantic and pragmatic knowledge that is linked to lexical representations and rapid access to this information during comprehension. A more complete understanding of the perceptual and computational capacities underlying spoken word recognition is essential to advancing understanding of both normal and deviant language acquisition and processing. Because the speech signal unfolds over time and the acoustic realization of a word varies with its local environment, it is important to evaluate spoken word recognition at a fine temporal grain using words embedded in continuous speech. This project has established "visual world" eye tracking as a powerful tool for examining spoken word recognition. The methods that we have developed are increasingly being used to address questions about spoken language processing in participant populations across the lifespan from infants to older adults, and in normal and impaired populations. The proposed research has two aims. The first aim is to evaluate a "data explanation" framework in which processing words in continuous speech is modulated by expectations based on context which (a) affect how listeners interpret the input and (b) provide a mechanism for rapid perceptual learning/adaptation. We manipulate speech rate and discourse-based information structure to examine how expectations affect real-time integration of asynchronous cues and how cue-weights are adjusted through perceptual learning. The second aim focuses on three emerging questions that affect the design, interpretation and analysis of visual world experiments: (1) Are the earliest signal-driven eye movements to pictures (at least partially) mediated by phonological information from displayed pictures or are eye-movements primarily mediated by perceptual representations activated by the spoken word; (2) What is the minimal lag between cues in the speech signal and the first stimulus-driven fixations; and (3) Are fixations affected b state-dependencies, and if so, under what conditions, and how can these effects be modeled within an event-based statistical framework.

描述（由申请人提供）：成年人可以识别50,000至100,000个单词，这是由不同的声音环境中的不同讲话者所说的。这种能力是显着的，因为语音随着一系列瞬态的声学事件的延伸而延伸了几百个毫秒，而对单词边界没有可靠的提示。尽管在普通人群中口语处理无处不在，但有5％的一年级学生进入学校，患有某种言语障碍，而听力障碍无法解释。此外，一旦成功获得了语言体系，它就容易受到伤害或中风的侮辱（在美国占100万成年人，并以某种形式的失语症）。口语识别在语言获取和口语理解中起着核心作用，可以存储丰富的句法，语义和务实知识，这些知识与词汇表示并在理解过程中迅速访问了此信息。对口语识别基础的感知和计算能力的更完整的理解对于促进对正常和偏差语言获取和处理的理解至关重要。由于语音信号随着时间的流逝而展开，并且单词的声学实现随其本地环境而变化，因此使用连续语音中嵌入的单词在细时粒上评估口语识别很重要。该项目已将“视觉世界”的眼动追踪作为检查口语识别的强大工具。我们开发的方法越来越多地用于解决有关从婴儿到老年人以及正常和受损人群中的参与者人群中的口语处理问题。拟议的研究有两个目标。第一个目的是评估一个“数据解释”框架，在该框架中，连续语音中的处理单词受到基于上下文的期望调节，（a）影响听众如何解释输入，（b）为快速感知学习/适应提供了一种机制。我们操纵语音率和基于话语的信息结构，以研究期望如何影响异步线索的实时整合，以及如何通过感知学习调整提示权重。第二个目的集中在影响视觉世界实验的设计，解释和分析的三个新兴问题上：（1）是最早的信号驱动的眼动向图片移动到图片（至少是部分）是由来自显示图片的语音信息所介导的，或者是主动介导的眼睛动态，主要是由Spoess Word激活的感知表示； （2）语音信号中的提示与第一个刺激驱动的固定位置之间的最小滞后是什么？ （3）是固定的影响B状态依赖性，如果是的，则在什么条件下，以及如何在基于事件的统计框架内建模这些效果。