Principal Investigator: Okim Kang
Organization: Northern Arizona University
NSF Award Information: EAGER: Collaborative Research: Production of Second Language Speech: Formulation of Objective Speech Intelligibility Measures and Learner-specific Feedback
This Early-concept Grants for Exploratory Research (EAGER) project focuses on exploring and developing a novel operational collection of speech, language and perception-based measures to objectively assess speech intelligibility for second language (L2) speech production, as well as providing effective learner-specific feedback. With the rise of English as an international language, intelligibility-based successful communication has been emphasized over native-like accents. However, L2 teachers often raise concerns about learners’ slow or stagnant pronunciation progress. Several primary reasons for this problem may include difficulties in perceptually discerning changes in learners’ speech and interpreting learners’ speech patterns without any learner-specific intelligibility assessment profile. Today, teachers have no systematic way to assess each student’s speech changes, nor can students monitor and track feedback related to their pronunciation learning progression. Therefore, an exploratory and transformative method is introduced for measuring speech intelligibility that provides both teachers and learners with objective and individualized feedback. This exploratory project is proposed for EAGER funding in order to establish a baseline working framework for operational objective measure creation, and proof-of-concept assessment feedback for teachers and learners. This approach will help teachers gauge learners’ intelligibility levels and allow learners to self-regulate their learning progress incrementally over time. The long-term innovation is expected to benefit skilled US professionals from non-English speaking countries, who work in various STEM (science, technology, engineering, and mathematics) fields. Additionally, this interdisciplinary project provides various opportunities for hands-on training and experience for both graduate and undergraduate students in the fields of language education, applied linguistics, computer engineering, and speech technology.
This project explores an idea to assess intelligibility in speech communications based on multiple individual speech measures for non-native speakers. The ideas are currently in their very early stages of development, and a large portion of the research ideas are untested. In order to establish the ground truth of potential individual speech production intelligibility measures, the implementation and feasibility of this intelligibility feedback approach must be validated with evidence. By employing advanced Automatic Speech Recognition-based accent classification technology based on machine learning, the team of researchers plan to provide learners with measured speech property information through operational and a discriminating set of objective speech intelligibility measures. The current innovation builds on language skill acquisition theory with a functional analytic-linguistic approach, arguing that explicit and metalinguistic feedback plays a pivotal role in moving learners forward in their L2 development. The vision is enabled by on-going research on auditory-based neurogram and spectrogram orthogonal polynomial measures that predict speech intelligibility, employing the learners’ unconstrained speech utterances. The project will contribute to the scientific knowledge of what constitutes L2 intelligible speech, understanding how individualized objective speech intelligibility feedback affects L2 speech development, and creating a foundational collection of speech/auditory/signal processing measures as well as ASR/DNN driven measures that assess a speaker’s intelligibility and identify efficient ways of implementing this technology in L2 learning 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.