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

该奖项是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。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名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致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在芝加哥大学的Melissa Baese-Berk博士的赞助下，这个博士后奖学金支持了一位研究重音语音感知学习的早期职业科学家。听众通常很难理解不熟悉的口音或方言，但随着他们对特定说话者的熟悉，理解力会迅速提高。然而，研究表明，这种学习是可能的和可行的，它是未知的，究竟是什么策略，听者依靠，以提高他们的理解。这个项目使用人工口音学习工具来研究听众如何学习适应口音讲话，以及如何反过来，这些知识可以用来使听众在新的听力环境中受益。通过这项研究，我们希望阐明听众如何能够成功地适应我们在真实的世界中遇到的言语语境的多样性，而不是期望所有的发言者听起来都一样。以这种方式规范语音变异性可以对来自不同语言背景的说话者产生重要的社会影响。尽管对语音的感知学习进行了几十年的研究，但听者对口音语音的适应机制仍然知之甚少。现有的研究主要使用两种方法之一来解决这个问题。第一种侧重于自然口音，而第二种则使用人工口音，通过操纵原本带有本地口音的语音中的单个目标声音来创建。两种方法都有问题。第一个模型更能代表听众面对的真实的世界任务，但对感知学习的机制提供了有限的见解，因为自然口音在许多不同的维度上都有所不同。相比之下，第二种方法给出了一个更清晰的想法，如何口音暴露改变特定的声音类别的看法，但代表了一个不太生态有效的情况。本项目将尝试使用成熟的研究方法（例如，词汇引导的重新校准），以调查在工作中的机制，在听众适应非母语口音的讲话。特别是，我们希望阐明高可变性训练协议和跨说话者可变性在实现强大的口音学习中的作用，这些口音学习可以在相关的听力环境中推广。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.