Ryuki Matsuura
ALSLA Doctoral Student
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, his 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.
Education
M.A., Engineering, Waseda University
B.A., International Liberal Studies, Waseda University
Areas of Interest
- L2 Speech
- AI/Machine Learning
- Emotion
- Computer Assisted Language Learning
Courses Taught at CMU
- 82-171 Elementary Japanese
- 82-173 Introduction to Japanese
Selected Awards and Honors
- 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
Selected Publications
Arai, Y., Matsuura, R., Eguchi, M., & Suzuki, S. (2026) Tracking the Longitudinal Change
of Flow Experience in an EFL Conversation Course. The Journal for the Psychology of Language
Learning.
Takatsu, H., Suzuki, S., Eguchi, M., Matsuura, R., Saeki, M., & Matsuyama, Y. (2026).
Gnowsis: Multimodal Multitask Learning for Oral Proficiency Assessments. Computer Speech &
Language.
Suzuki, S., Takatsu, H., Matsuura, R., Koyama, M., Saeki, M., & Matsuyama, Y. (2025).
Feedforwarding Diagnostic Language Assessment: AI-Driven Weakness Identification and
Contextualised Feedback for L2 Speaking. Language Testing.
https://doi.org/10.1177/02655322251348725
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/