Phonetic adaptation as (near) rational learning in a variable environment

语音适应作为可变环境中的（近）理性学习

• 批准号: 8479653
• 负责人: Tim Florian Jaeger
• 金额: $ 31.19万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-05 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8479653
• 关键词: Accent; Accounting; Acoustics; Adult; Agreement; Aphasia; Behavioral; Belief; Biological Process; Childhood; Cognitive; Computer Simulation; Cues; Data; Development; Disease; Environment; Exhibits; General Population; Goals; Injury; Investigation; Joints; Knowledge; Label; Language; Language Development; Learning; Linguistics; Maps; Mastication; Modeling; Online Systems; Participant; Peach; Perception; Phonetics; Process; Property; Research; Resources; Schools; Signal Transduction; Speech; Speech Perception; Speech Sound; Speed; Stimulus; Stroke; System; Testing; Time; Uncertainty; Update; Vision; Work; computer framework; deviant; experience; hearing impairment; language processing; model development; motor control; novel; particle; phonology; public health relevance; research study; response; social; sound; success

DESCRIPTION (provided by applicant): Understanding speech is a remarkable ability: listeners need to extract asynchronously distributed phonological features from a transient and noisy signal that unfolds at speeds not under their control. Despite the ubiquitous success of spoken language processing among the general population, 5% 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). One of the biggest challenges that speech understanding has to overcome is that speakers differ in how exactly they realize the same intended sound, so that one speaker's 'peach' might by physically indistinguishable from another speaker's 'beach'. Research suggests that we overcome this challenge by rapidly adapting to speaker-specific pronunciations. A more complete understanding of the perceptual and computational capacities underlying these adaptation processes is essential to advancing understanding of both normal and deviant language acquisition and processing. The aim of the proposed project is to develop and test an explicit computational model of the cognitive processes that underlie our ability to rapidly adjust to speaker-specific pronunciations ('accents'). To this end, we investigate how listeners integrate information about speakers' accents while listening to their speech. We propose that listeners weigh previous experience with other speakers against the percepts received from the current speaker. We investigate adaptation to a single speaker, adaptation to multiple speakers, and generalization from previously encountered speakers to novel speakers. The computational framework we pursue, also predicts that alternative reasons for unexpected pronunciations (such as chewing on a piece of gum), if present, can block adaptation effects. We investigate and model such 'explaining away' of unusual pronunciations. To collect large amounts of adaptation data in a fraction of the time previously required for experiments on speech perception, we have developed a web-based experimental interface that allows us to reach hundreds of thousands of participants.

描述（由申请人提供）：理解语音是一种非凡的能力：听众需要从以不受他们控制的速度展开的瞬态和噪声信号中提取异步分布的语音特征。尽管口语处理在普通人群中普遍取得了成功，但5%的一年级学生入学时患有某种类型的言语声音障碍，无法通过听力障碍来解释。此外，一旦语言系统已经成功地获得，它很容易受到伤害或中风的侮辱（在美国有100万成年人患有某种形式的失语症）。语音理解必须克服的最大挑战之一是，说话者在如何准确地实现相同的预期声音方面存在差异，因此一个说话者的“桃子”可能与另一个说话者的“海滩”在物理上无法区分。研究表明，我们可以通过快速适应说话者特定的发音来克服这一挑战。更全面地了解这些适应过程背后的感知和计算能力对于促进正常和异常语言习得和处理的理解至关重要。该项目的目的是开发和测试一个明确的认知过程的计算模型，该模型是我们快速调整能力的基础 特定于说话者的发音（“口音”）。为此，我们调查了听众如何整合信息的扬声器的口音，而听他们的讲话。我们建议听众权衡以前的经验与其他扬声器对当前扬声器收到的感知。我们调查适应一个单一的扬声器，适应多个扬声器，并从以前遇到的扬声器到新的扬声器泛化。我们所追求的计算框架还预测，意外发音的其他原因（例如咀嚼一块口香糖），如果存在，可以阻止适应效应。我们调查和模型这样的'解释了'不寻常的发音。为了在语音感知实验所需的一小部分时间内收集大量的适应数据，我们开发了一个基于网络的实验界面，使我们能够接触到数十万的参与者。