Developing a Multilingual Spontaneous L2 Speech Corpus for Automated Proficiency Assessment
Seunghee Han, Sunhee Kim, Minhwa Chung · 2024
Currently, most accessible multilingual L2 spontaneous speech corpora primarily include L2 English from speakers of various L1 backgrounds, with few and often small-scale corpora available for non-English L2s. Annotated assessment data from expert raters is especially rare. This study addresses this gap by constructing a large-scale dataset of spontaneous L2 speech in seven languages (Chinese, Japanese, English, French, German, Spanish, and Russian) from Korean L1 speakers, accompanied by detailed assessments using a carefully designed rubric. The dataset includes extensive metadata analysis and validation processes to ensure the reliability of subjective assessments. To our knowledge, this is the first large-scale, comprehensive corpus of its kind, featuring diverse L2 spontaneous speech from Korean speakers with expert-annotated assessments.