2024/06/07 by Khazar Khorrami, Khorrami, Khazar, Okko Räsänen +1 · 2 citations
Arts and Humanities · Computer Science · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #Speech and dialogue systems #Subtitles and Audiovisual Media #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.05259
openalex publication_date 2024/06/07 · openalex created_date 2024/06/12 · openalex updated_date 2026/07/28
Infants gradually learn to parse continuous speech into words and connect names with objects, yet the mechanisms behind development of early word perception skills remain unknown. We studied the extent to which early words can be acquired through statistical learning from regularities in audiovisual sensory input. We simulated word learning in infants up to 12 months of age in a realistic setting, using a model that solely learns from statistical regularities in unannotated raw speech and pixel-level visual input. Crucially, the quantity of object naming events was carefully designed to match that accessible to infants of comparable ages. Results show that the model effectively learns to recognize words and associate them with corresponding visual objects, with a vocabulary growth rate comparable to that observed in infants. The findings support the viability of general statistical learning for early word perception, demonstrating how learning can operate without assuming any prior linguistic capabilities.