@inproceedings{oneil-etal-2024-developing,
    title = "Developing a Benchmark for Pronunciation Feedback: Creation of a Phonemically Annotated Speech Corpus of isi{Z}ulu Language Learner Speech",
    author = "O{'}Neil, Alexandra  and
      Hjortnaes, Nils  and
      Tyers, Francis  and
      Nkosi, Zinhle  and
      Ndlovu, Thulile  and
      Mlondo, Zanele  and
      Pewa, Ngami Phumzile",
    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.429",
    pages = "4795--4801",
    abstract = "Pronunciation of the phonemic inventory of a new language often presents difficulties to second language (L2) learners. These challenges can be alleviated by the development of pronunciation feedback tools that take speech input from learners and return information about errors in the utterance. This paper presents the development of a corpus designed for use in pronunciation feedback research. The corpus is comprised of gold standard recordings from isiZulu teachers and recordings from isiZulu L2 learners that have been annotated for pronunciation errors. Exploring the potential benefits of word-level versus phoneme-level feedback necessitates a speech corpus that has been annotated for errors on the phoneme-level. To aid in this discussion, this corpus of isiZulu L2 speech has been annotated for phoneme-errors in utterances, as well as suprasegmental errors in tone.",
}

Pronunciation of the phonemic inventory of a new language often presents difficulties to second language (L2) learners. These challenges can be alleviated by the development of pronunciation feedback tools that take speech input from learners and return information about errors in the utterance. This paper presents the development of a corpus designed for use in pronunciation feedback research. The corpus is comprised of gold standard recordings from isiZulu teachers and recordings from isiZulu L2 learners that have been annotated for pronunciation errors. Exploring the potential benefits of word-level versus phoneme-level feedback necessitates a speech corpus that has been annotated for errors on the phoneme-level. To aid in this discussion, this corpus of isiZulu L2 speech has been annotated for phoneme-errors in utterances, as well as suprasegmental errors in tone.
O{'}Neil, Alex ra Hjortnaes, Nils Tyers, Francis Nkosi, Zinhle Ndlovu, Thulile Mlondo, Zanele Pewa, Ngami Phumzile
Developing a Benchmark for Pronunciation Feedback: Creation of a Phonemically Annotated Speech Corpus of isiZulu Language Learner Speech