Lost but not erased: Finding traces of a forgotten language in neural speech models
Read the original on arXiv Machine Learning →The study investigates whether phonological traces of a first language persist in neural speech models after switching to a second language, mirroring phenomena observed in international adoptees. Using automatic speech recognition models trained on one language and then abruptly switched to another, researchers found that traces of the first language remained in the lowest, pre‑phonemic layers throughout second‑language training. These traces proved functional, as models with early exposure re‑learned their lost first language 14% faster than naive models, an advantage that vanished when the earliest layers were replaced with those from a non‑adopted model.
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