西班牙语-英语双语学前儿童日常语言输入输出自动识别

Measuring how much of each language bilingual children hear and use in their daily lives requires transcribing long, naturalistic recordings, which is too costly to do by hand at scale. We benchmarked four WhisperX-based automatic speech recognition pipelines, differing in how speech is located and attributed to speakers, against expert transcripts of 168 five-minute clips (14 hours) drawn from daylong home recordings of 14 Spanish--English bilingual preschoolers. Word error rates across pipelines were high (69.5% to 82.0% overall), compared with 35.4% (key child) and 53.9% (adults) between ex