The Learning Objective Governs Perceptual Narrowing: A Cross-Lingual, Layer-Wise, Ten-Seed Study of Self-Supervised Speech Encoders
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Abstract (arXiv)
Perceptual narrowing---the developmental loss of non-native phoneme discrimination in the first year of life citepwerker1984---is a canonical developmental finding, yet what learning objective produces it remains open. We train a (~)7,M-parameter Transformer encoder on child-directed and read speech and evaluate phoneme ABX in English, French, and Mandarin over ten seeds, the seed as the unit of replication. Six results. (1)~The objective sets the direction of cross-lingual transfer: reconstruction (masked mel-prediction) degrades non-native discrimination, prediction (frame-contrastive) improves it---a same-encoder, same-data gap of (+0.051) in first-layer Mandarin ABX ((p=3×10-8)), unanimous in sign across twenty runs. (2)~That decline combines a large arm-intrinsic difficulty gradient with a smaller language-specialization effect (matched vs. mismatched (+0.022), (p=10-4), all four layers). (3)~Against a language-symmetric raw-mel floor, reconstruction pushes the first layer below the discriminability of its input; prediction pushes it above. (4)~Read speech gives a (3.6×) steeper non-native decline than child-directed speech. (5)~The customary three-seed budget cannot see this reliably: an effect unambiguous at ten seeds is called significant by as few as 70% of three-seed subsets. (6)~Six objective configurations---sharpening, compression, consolidation, their composition, and word-level semantic grounding in two forms---fail to produce the full developmental signature (native improves and non-native declines): a single objective moves both languages the same way because it acts on a shared representation. We conclude that the objective, not the architecture, is the first-order determinant of narrowing-shaped representational change.
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References · 7
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
- 2025Nested Learning: The Illusion of Deep Learning Architecturescited by 72
- 2022Do self-supervised speech models develop human-like perception biases?cited by 28
- 2026Computational modeling of early language learning from acoustic speech and audiovisual input without linguistic priorscited by 1
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