SpInt is a speech intelligibility dataset designed for the evaluation of objective intelligibility metrics on Spanish speech. The dataset contains: Complex spectral masks describing the effect of speech enhancement processing. Noise signals. Intelligibility labels obtained from human listening experiments. Metadata describing each stimulus and listener response. Reconstruction script. The dataset was developed using speech material from the Spanish Matrix Test (OLSA corpus) and includes listening-test results obtained under different noisy conditions and speech enhancement systems. To respect the licensing conditions of the Spanish Matrix Test corpus, the original clean speech recordings are not distributed as part of SpInt. A detailed description of the dataset design, listening-test methodology, speech enhancement systems, objective intelligibility metric evaluation framework, and experimental results can be found in: Iván López-Espejo and Jesper Jensen,"An Objective Intelligibility Metric Evaluation on Spanish Speech",Proceedings of IberSPEECH 2026. Users interested in the rationale behind the dataset construction and experimental protocol are encouraged to consult the accompanying publication.