Carnegie Mellon University
Dietrich College of Humanities and Social Sciences  ›  Department of Languages, Cultures & Applied Linguistics  ›  About Us  ›  View All  ›  Ph.D. Students  ›  Ryuki Matsuura

Ryuki Matsuura

Ryuki Matsuura

Education

M.A., Engineering, Waseda University
B.A., International Liberal Studies, Waseda University

Bio

Ryuki Matsuura is a doctoral student in the Applied Linguistics & Second Language Acquisition (ALSLA) program. His research interests lie in the effective assessment, feedback and learning assistance of L2 speech utilizing AI techniques. More specifically, he has conducted research on automated scoring systems for L2 oral fluency.

During his master’s program, he was involved in a research project on AI conversational agent for L2 English testing and examined the impacts of an AI-driven diagnostic assessment for L2 spoken vocabulary. Extending this line of research, her doctoral study aims to develop and validate spoken dialogue systems that assist the learning of L2 productive knowledge and skills.

Additionally, he is interested in advancing research methodologies in second language acquisition (SLA) using state-of-the-art AI technologies. He developed a system to automatically annotate the full range of oral fluency features (i.e., syllables, disfluency words and pause locations). He is open to collaborating with researchers worldwide by automatizing L2 data annotations and analyses.

  • L2 Speech
  • AI/Machine Learning
  • Computer Assisted Language Learning
  • Feedback
  • Individual Differences
  • Japan Society for the Promotion of Science Doctoral Course Research Fellowship (declined)
  • Sponsorship Award of SIG-SLP (Spoken Language Processing), 2022
  • Student Presentation Award, Acoustic Society of Japan, 2022

Suzuki, S., Takatsu, H., Matsuura, R., Koyama, M., Saeki, M., & Matsuyama, Y. (in press). Feedforwarding diagnostic language assessment: AI-driven weakness identification and contextualised feedback for L2 speaking. Language Testing.

Matsuura, R., Suzuki, S., Takizawa, K., Saeki, M., & Matsuyama, Y. (2025). Gauging the Validity of Machine Learning-Based Temporal Feature Annotation to Measure Fluency in Speech Automatically. Research Methods in Applied Linguistics, 4(1), 1–23. https://doi.org/10.1016/j.rmal.2024.100177

Saeki, M., Takatsu, H., Kurata, F., Suzuki, S., Eguchi, M., Matsuura, R., Takizawa, K., Yoshikawa, S., & Matsuyama, Y. (2024). InteLLA: Intelligent Language Learning Assistant for Assessing Language Proficiency Through Interviews and Roleplays. Proc. of SIGDIAL 2024, 385-399. https://aclanthology.org/2024.sigdial-1.34/

Matsuura, R. & Suzuki, S. (2023). Prompt-independent Automated Scoring of L2 Oral Fluency by Capturing Prompt Effects. Proc. of Artificial Intelligence in Education (AIED). https://doi.org/10.1007/978-3-031-36272-9_62

Matsuura, R., Suzuki, S., Saeki, M., Ogawa, T., & Matsuyama, Y. (2022). Refinement of Utterance Fluency Feature Extraction and Automated Scoring of L2 Oral Fluency with Dialogic Features. Proc. of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). 1312-1320. https://doi.org/10.23919/APSIPAASC55919.2022.9980148

Department Member Since 2024