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

该奖项是作为NSF社会、行为和经济学博士后研究奖学金(SPRF)和语言学项目的一部分提供的。SPRF计划的目标是为学术界、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项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。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在芝加哥大学Melissa Baese-Berk博士的赞助下，这一博士后奖学金奖项支持一位研究重音言语知觉学习的早期职业科学家。听者通常难以理解不熟悉的口音或方言，但随着他们对特定说话者变得更加熟悉，理解能力可以迅速提高。然而，研究表明，这种学习是可能和可行的，目前还不清楚听者到底依靠什么策略来提高他们的理解力。这个项目使用人工口音学习工具来调查听者如何学习适应带口音的语音，以及如何反过来利用这些知识在新的听力环境中造福于听者。通过这项研究，我们希望阐明听者如何成功地适应我们在现实世界中遇到的不同的演讲语境，而不是期望所有的说话者听起来都一样。以这种方式使语音变异性正常化对来自不同语言背景的说话者具有重要的社会影响。尽管对语音的知觉学习进行了几十年的研究，但听者对重音语音适应的机制仍然知之甚少。现有的研究大多使用两种方法中的一种来解决这个问题。第一种侧重于自然口音，而第二种则使用了人工口音，这种口音是通过操纵个别目标声音而产生的，这些声音本来就是母语口音。这两种方法都有问题。第一种更能代表听者面对的真实世界--任务，但对知觉学习的机制提供的洞察力有限，因为自然口音在许多不同的维度上不同。相比之下，第二种方法给出了一个更清晰的想法，即口音暴露如何改变人们对特定声音类别的感知，但代表了一种不那么生态有效的情景。本项目试图通过使用成熟的研究方法(例如，词汇引导的再校准)来研究听者对非母语重音言语的顺应机制，试图在这些平行文献之间架起桥梁。特别是，我们希望阐明高变异性培训方案和交叉说话者变异性在实现健壮的口音学习方面的作用，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优势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.