@inproceedings{ferro-etal-2024-readlet,
    title = "{R}ead{L}et: A Dataset for Oral, Visual and Tactile Text Reading Data of Early and Mature Readers",
    author = "Ferro, Marcello  and
      Marzi, Claudia  and
      Nadalini, Andrea  and
      Taxitari, Loukia  and
      Lento, Alessandro  and
      Pirrelli, Vito",
    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.1188",
    pages = "13595--13609",
    abstract = "The paper presents the design and construction of a time-stamped multimodal dataset for reading research, including multiple time-aligned temporal signals elicited with four experimental trials of connected text reading by both child and adult readers. We present the experimental protocols, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of a time-aligned multimodal dataset for reading research, we present a few statistical analyses showing the correlation and complementarity of multimodal time-series of reading data, as well as some results of modelling adults{'} reading data by integrating different modalities. The total dataset size amounts to about 2.5 GByte in compressed format.",
}

The paper presents the design and construction of a time-stamped multimodal dataset for reading research, including multiple time-aligned temporal signals elicited with four experimental trials of connected text reading by both child and adult readers. We present the experimental protocols, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of a time-aligned multimodal dataset for reading research, we present a few statistical analyses showing the correlation and complementarity of multimodal time-series of reading data, as well as some results of modelling adults{'} reading data by integrating different modalities. The total dataset size amounts to about 2.5 GByte in compressed format.
Ferro, Marcello Marzi, Claudia Nadalini, Andrea Taxitari, Loukia Lento, Aless ro Pirrelli, Vito
ReadLet: A Dataset for Oral, Visual and Tactile Text Reading Data of Early and Mature Readers