EXP: Collaborative Research: Perception and Production in Second Language: The Roles of Voice Variability and Familiarity

EXP：协作研究：第二语言的感知和产生：语音变异性和熟悉度的作用

• 批准号: 1623750
• 负责人: Ricardo Gutierrez-Osuna
• 金额: $ 28.9万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1623750&HistoricalAwards=false
• 关键词: EXP; Collaborative; Research; Perception; Production

The U.S. economy is boosted by many highly skilled professionals from non-English speaking countries, who work in higher education, healthcare, and technology. These professionals typically have solid knowledge of English but age-related non-native pronunciations that can impair their intelligibility. Highly-skilled professions require advanced oral communication skills, including highly intelligible pronunciation; because such professionals come from a variety of first languages, their pronunciation needs are highly individualized. Thus, traditional classroom instruction cannot easily meet the needs of diverse learners nor allow for significant amounts of one-on-one instruction. To address this issue, the investigators propose an innovative genre of technology for computer-assisted pronunciation training (CAPT) that allows learners to practice at their own pace, and as often as they wish, with a personalized voice model, increasing the likelihood of improved intelligibility.Specifically, the approach builds on research showing that second-language (L2) learners are more likely to succeed when imitating a similar voice, a so-called golden speaker. The proposed CAPT technology provides an ideal golden speaker for each learner: their own voice but with a native accent through a digital learning environment that allows L2 learners to build their golden speakers incrementally and interactively by selecting among different regional US accents. This vision is enabled by on-going NSF-sponsored research on accent-conversion algorithms to digitally alter L2 utterances with suprasegmental and segmental features from a reference native speaker. This Cyberlearning project brings accent-conversion techniques into pronunciation instruction in the form of a web-based interactive learning tool. The project uses a design-based research paradigm to (1) gain knowledge of how people learn L2 pronunciation skills while interacting with computational golden speakers; and (2) identify efficient ways of implementing this technology in L2 learning contexts. The project focuses initially on Spanish learners of English, later generalizing the approach to other first language speakers.

美国经济得到了许多来自非英语国家的高技能专业人士的推动，他们在高等教育、医疗保健和技术领域工作。这些专业人士通常拥有扎实的英语知识，但与年龄相关的非母语发音可能会损害他们的理解力。高技能的职业需要高级的口语交流技能，包括高度可理解的发音；因为这些专业人员来自各种第一语言，他们的发音需求高度个性化。因此，传统的课堂教学不能很容易地满足不同学习者的需求，也不能进行大量的一对一教学。为了解决这个问题，研究人员提出了一种创新的计算机辅助发音训练(CAPT)技术类型，允许学习者按照自己的节奏和频率使用个性化的语音模型进行练习，从而增加提高理解力的可能性。具体地说，该方法建立在研究表明，第二语言(L2)学习者在模仿相似的声音时更有可能成功，即所谓的黄金说话者。拟议的CAPT技术为每个学习者提供了理想的黄金扬声器：他们自己的声音，但通过数字学习环境具有母语口音，允许第二语言学习者通过在不同地区的美国口音中进行选择，逐步和互动地建立他们的黄金扬声器。这一愿景是由NSF赞助的口音转换算法的持续研究实现的，该算法可以数字地改变来自参考母语说话者的具有超段和分段特征的二语发音。这个网络学习项目以基于网络的互动学习工具的形式将口音转换技术引入语音教学。该项目使用基于设计的研究范式来(1)获得人们如何在与计算黄金说话者互动时学习第二语言发音技能的知识；以及(2)确定在第二语言学习环境中实施这项技术的有效方法。该项目最初侧重于西班牙语英语学习者，后来将该方法推广到其他母语为母语的人。