@inproceedings{han-etal-2024-constructing,
    title = "Constructing {K}orean Learners{'} {L}2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment",
    author = "Han, Seunghee  and
      Kim, Sunhee  and
      Chung, Minhwa",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.334",
    pages = "3772--3781",
    abstract = "Multilingual L2 speech corpora for developing automatic speech assessment are currently available, but they lack comprehensive annotations of L2 speech from non-native speakers of various languages. This study introduces the methodology of designing a Korean learners{'} L2 speech corpus of seven languages: English, Japanese, Chinese, French, German, Spanish, and Russian. We describe the development of reading scripts, reading tasks, scoring criteria, and expert evaluation methods in detail. Our corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each for French, German, Spanish, and Russian). The corpus is annotated with spelling and pronunciation transcription, expert pronunciation assessment scores (accuracy of pronunciation and fluency of prosody), and metadata such as gender, age, self-reported language proficiency, and pronunciation error types. We also propose a practical verification method and a reliability threshold to ensure the reliability and objectivity of large-scale subjective evaluation data.",
}

Multilingual L2 speech corpora for developing automatic speech assessment are currently available, but they lack comprehensive annotations of L2 speech from non-native speakers of various languages. This study introduces the methodology of designing a Korean learners{'} L2 speech corpus of seven languages: English, Japanese, Chinese, French, German, Spanish, and Russian. We describe the development of reading scripts, reading tasks, scoring criteria, and expert evaluation methods in detail. Our corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each for French, German, Spanish, and Russian). The corpus is annotated with spelling and pronunciation transcription, expert pronunciation assessment scores (accuracy of pronunciation and fluency of prosody), and metadata such as gender, age, self-reported language proficiency, and pronunciation error types. We also propose a practical verification method and a reliability threshold to ensure the reliability and objectivity of large-scale subjective evaluation data.
Han, Seunghee Kim, Sunhee Chung, Minhwa
Constructing Korean Learners' L2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment