@inproceedings{hernandez-mena-etal-2024-samromur,
    title = "{S}amr{\'o}{M}ur {M}illj{\'o}{N}: An {ASR} Corpus of One Million Verified Read Prompts in {I}celandic",
    author = "Hernandez Mena, Carlos Daniel  and
      Gunnarsson, {\TH}orsteinn Da{\dh}i  and
      Gudnason, Jon",
    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.1246",
    pages = "14305--14312",
    abstract = "The platform samromur.is, or {``}Samr{\'o}mur{''} for short, is a crowdsourcing web application built on Mozilla{'}s Common Voice, designed to accumulate speech data for the advancement of language technologies in Icelandic. Over the years, Samr{\'o}mur has proven to be remarkably successful in amassing a significant number of high-quality audio clips from thousands of users. However, the challenge of manually verifying the entirety of the collected data has hindered its effective exploitation, especially in the realm of Automatic Speech Recognition (ASR), its original purpose. In this paper, we introduce the {``}Samr{\'o}mur Millj{\'o}n{''} corpus, an ASR dataset comprising one million audio clips from Samr{\'o}mur. These clips have been automatically verified using state-of-the-art speech recognition systems such as NeMo, Wav2Vec2, and Whisper. Additionally, we present the ASR results obtained from creating acoustic models based on Samr{\'o}mur Millj{\'o}n. These results demonstrate significant promise when compared to other acoustic models trained with a similar volume of Icelandic data from different sources.",
}

The platform samromur.is, or {``}Samr{\'o}mur{''} for short, is a crowdsourcing web application built on Mozilla{'}s Common Voice, designed to accumulate speech data for the advancement of language technologies in Icelandic. Over the years, Samr{\'o}mur has proven to be remarkably successful in amassing a significant number of high-quality audio clips from thousands of users. However, the challenge of manually verifying the entirety of the collected data has hindered its effective exploitation, especially in the realm of Automatic Speech Recognition (ASR), its original purpose. In this paper, we introduce the {``}Samr{\'o}mur Millj{\'o}n{''} corpus, an ASR dataset comprising one million audio clips from Samr{\'o}mur. These clips have been automatically verified using state-of-the-art speech recognition systems such as NeMo, Wav2Vec2, and Whisper. Additionally, we present the ASR results obtained from creating acoustic models based on Samr{\'o}mur Millj{\'o}n. These results demonstrate significant promise when compared to other acoustic models trained with a similar volume of Icelandic data from different sources.
Hern ez Mena, Carlos Daniel Gunnarsson, {\TH}orsteinn Da{\dh}i Gudnason, Jon
SamróMur MilljóN: An ASR Corpus of One Million Verified Read Prompts in Icelandic