Postdoctoral Fellowship: SPRF: Mechanisms Underlying Perceptual Learning of Accented Speech

博士后奖学金：SPRF：口音感知学习的机制

• 批准号: 2303087
• 负责人: Yevgeniy Melguy
• 金额: $ 16万
• 依托单位: Melguy, Yevgeniy
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2303087&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; SPRF; Mechanisms; Underlying

This award was provided as part of the NSF Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) and Linguistics programs. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Melissa Baese-Berk at the University of Chicago, this postdoctoral fellowship award supports an early career scientist investigating perceptual learning of accented speech. Listeners often have trouble understanding an unfamiliar accent or dialect, but comprehension can rapidly improve as they become more familiar with a given speaker. However, research shows that such learning is possible and feasible, it is unknown exactly which strategies listeners rely on in order to improve their comprehension. This project uses artificial accent learning tools to investigate how listeners learn to adapt to accented speech, and how in turn this knowledge may be used to benefit listeners in novel listening contexts. Through this study, we hope to shed light on how listeners can successfully adapt to the diversity of speech contexts that we encounter in the real world, rather than expecting all speakers to sound the same. Normalizing speech variability in this way can have important societal ramifications for speakers who come from diverse linguistic backgrounds.Despite several decades of research on perceptual learning for speech, the mechanisms that underlie listener adaptation to accented speech are still poorly understood. Existing research has largely addressed this question using one of two approaches. The first has focused on natural accents, whereas the second has used artificial accents, created by manipulating individual target sounds in otherwise natively-accented speech. Both approaches have issues. The first is much more representative of the real world-task listeners face, but offers limited insight into the mechanisms of perceptual learning, because natural accents differ on many different dimensions. By contrast, the second approach gives a clearer idea of how accent exposure changes perception of specific sound categories but represents a less ecologically-valid scenario. This project would attempt to bridge these parallel literatures, using well-established research methods (e.g., lexically-guided recalibration) to investigate the mechanisms at work in listener adaptation to non-natively accented speech. In particular, we hope to shed light on the role of high-variability training protocols and cross-speaker variability in achieving robust accent learning that can generalize across related listening contexts.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.

该奖项是作为NSF社会，行为和经济科学博士后研究奖学金（SPRF）和语言学计划的一部分提供的。 SPRF计划的目标是为学术界，工业或私营部门以及政府的科学职业准备有前途的早期职业博士学位科学家。 SPRF奖项涉及在既定科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。 NSF试图促进科学界各个细分市场的科学家的参与，包括来自代表性不足的群体的研究计划和活动；博士后时期被认为是实现这一目标的重要水平。每个博士后研究员都必须解决重要的科学问题，以推进各自的学科领域。在芝加哥大学的梅利莎·贝塞·贝克（Melissa Baese-Berk）博士的赞助下，该博士后奖学金奖支持了早期的职业科学家，调查了重音演讲的知觉学习。听众通常很难理解一种陌生的口音或方言，但是随着对给定的演讲者的熟悉，理解可以迅速改善。但是，研究表明，这种学习是可能的和可行的，目前尚不清楚听众为了提高理解而依靠哪些策略。该项目使用人造口音学习工具来研究听众如何学会适应重音语音，而这些知识又如何用于使听众在新颖的听力环境中受益。通过这项研究，我们希望阐明听众如何成功适应我们在现实世界中遇到的语音背景的多样性，而不是期望所有演讲者听起来都一样。以这种方式使语音变异归一化可以对来自不同语言背景的说话者产生重要的社会影响。尽管在言语的感知学习方面进行了数十年的研究，但基于听众适应强调言语的机制仍然知之甚少。现有的研究在很大程度上使用了两种方法之一解决了这个问题。第一个重点是自然口音，而第二个则使用了人工色调，该口音是通过操纵单个目标的声音在原本良好的语音中创建的。两种方法都有问题。第一个是现实世界任务听众面对的更具代表性的代表，但是对感知学习机制的洞察力有限，因为自然口音在许多不同的方面有所不同。相比之下，第二种方法给出了更清晰的想法，即重音暴露如何改变对特定声音类别的感知，但代表了一种较少的生态播种情况。该项目将尝试使用良好的研究方法（例如，词汇引导的重新校准）来研究这些平行文献，以研究听众适应非本地重音语音的工作机制。特别是，我们希望阐明高变异性培训协议和跨语言变异性在实现强大的口音学习中的作用，这可以跨越相关的听力环境。该奖项反映了NSF的法定任务，并被认为是值得通过评估的支持。利用基金会的知识分子和更广泛的影响审查标准。