ÌròyìnSpeech: A Multi-purpose Yorùbá Speech Corpus

Tolulope Ogunremi, Kola Tubosun, Anuoluwapo Aremu, Iroro Orife, David Ifeoluwa Adelani

Abstract
We introduce ÌròyìnSpeech corpus—a new dataset influenced by a desire to increase the amount of high quality, freely available, contemporary Yorùbá speech data that can be used for both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. We curated about 23,000 text sentences from the news and creative writing domains with an open license i.e., CC-BY-4.0 and asked multiple speakers to record each sentence. To encourage more participatory approach to data creation, we provide 5 000 utterances from the curated sentences to the Mozilla Common Voice platform to crowd-source the recording and validation of Yorùbá speech data. In total, we created about 42 hours of speech data recorded by 80 volunteers in-house, and 6 hours validated recordings on Mozilla Common Voice platform. Our evaluation on TTS shows that we can create a good quality general domain single-speaker TTS model for Yorùbá with as little 5 hours of speech by leveraging an end-to-end VITS architecture. Similarly, for ASR, we obtained a WER of 21.5.
Anthology ID:
2024.lrec-main.812
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
9296–9303
Language:
URL:
https://aclanthology.org/2024.lrec-main.812
DOI:
Bibkey:
Cite (ACL):
Tolulope Ogunremi, Kola Tubosun, Anuoluwapo Aremu, Iroro Orife, and David Ifeoluwa Adelani. 2024. ÌròyìnSpeech: A Multi-purpose Yorùbá Speech Corpus. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9296–9303, Torino, Italia. ELRA and ICCL.
Cite (Informal):
ÌròyìnSpeech: A Multi-purpose Yorùbá Speech Corpus (Ogunremi et al., LREC-COLING 2024)
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