  This paper presents VoxCeleb-ESP, a collection of pointers and timestamps to
YouTube videos facilitating the creation of a novel speaker recognition
dataset. VoxCeleb-ESP captures real-world scenarios, incorporating diverse
speaking styles, noises, and channel distortions. It includes 160 Spanish
celebrities spanning various categories, ensuring a representative distribution
across age groups and geographic regions in Spain. We provide two speaker trial
lists for speaker identification tasks, each of them with same-video or
different-video target trials respectively, accompanied by a cross-lingual
evaluation of ResNet pretrained models. Preliminary speaker identification
results suggest that the complexity of the detection task in VoxCeleb-ESP is
equivalent to that of the original and much larger VoxCeleb in English.
VoxCeleb-ESP contributes to the expansion of speaker recognition benchmarks
with a comprehensive and diverse dataset for the Spanish language.
