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

PI: Ricardo Gutierrez-Osuna, Texas A&M (Award Details)
PI: John Levis, Iowa State University (Award Details)

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.

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