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

美国经济受到来自非英语国家的许多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。这些专业人士通常具有扎实的英语知识，但与年龄相关的非母语发音可能会损害他们的可理解性。高技能的职业需要先进的口头交流技能，包括高度可理解的发音;因为这些专业人士来自各种第一语言，他们的发音需求是高度个性化的。因此，传统的课堂教学不能轻易满足不同学习者的需求，也不允许大量的一对一教学。 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，允许学习者按照自己的节奏练习，并根据他们的意愿，使用个性化的语音模型，增加提高可理解性的可能性。具体来说，该方法建立在研究基础上，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，所谓的黄金演讲者。建议CAPT技术为每个学习者提供了一个理想的黄金扬声器：他们自己的声音，但通过数字学习环境，允许L2学习者建立自己的黄金扬声器增量和交互式选择不同的区域美国口音的本地口音。 这一愿景是由国家科学基金会赞助的正在进行的口音转换算法的研究，以数字方式改变L2的话语与超音段和音段功能，从参考母语。 这一网络学习项目以网络互动学习工具的形式将口音转换技术纳入发音教学。该项目使用基于设计的研究范式，以（1）获得人们如何学习L2发音技能的知识，同时与计算黄金扬声器进行交互;（2）确定在L2学习环境中实施这项技术的有效方法。该项目最初侧重于英语的西班牙语学习者，后来将该方法推广到其他第一语言使用者。

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.