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Lost but not erased: Finding traces of a forgotten language in neural speech models

arXiv 2608.25976 ↗First posted 26 Aug 2026cs.CL cs.LGcited by 0

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Abstract (arXiv)

International adoptees retain phonological traces of a birth language they can no longer speak or comprehend, a persistence typically attributed to a biologically-timed critical period. We asked whether it could instead reflect the ordinary dynamics of learning, using automatic speech recognition models that simulate the international adoptee experience without maturational confounds. Models were trained on one language and then abruptly switched to a second. We found that traces of the first language persisted throughout second-language training, but mainly in the lowest, pre-phonemic layers. These traces were functional, as models with early exposure re-learned their lost first language 14% faster than naive models; this advantage held even against models adopted early from a related language and disappeared when the earliest layers were substituted from a non-adopted model. We argue that these critical-period effects reflect entrenchment of foundational representations rather than a maturational loss of plasticity, and that experience plays a central role in critical periods in language acquisition.

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References · 9

  1. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  2. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  3. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  4. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  5. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  6. 2017Critical Learning Periods in Deep Neural Networkscited by 78
  7. 2024Investigating Critical Period Effects in Language Acquisition through Neural Language Modelscited by 15
  8. 2022Critical Learning Periods for Multisensory Integration in Deep Networkscited by 14
  9. 2021How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddingscited by 8

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