Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?

• URL: http://arxiv.org/abs/2508.21210v1
• Date: Thu, 28 Aug 2025 20:56:16 GMT
• Title: Do Self-Supervised Speech Models Exhibit the Critical Period Effects in Language Acquisition?
• Authors: Yurie Koga, Shunsuke Kando, Yusuke Miyao,
• Abstract summary: Critical Period (CP) effects in human language acquisition are observed in self-supervised speech models (S3Ms)<n>CP effects refer to greater difficulty in acquiring a second language (L2) with delayed L2 exposure onset, and greater retention of their first language (L1) with delayed L1 exposure offset.<n>We train S3Ms with varying L2 training onsets and L1 training offsets on child-directed speech and evaluate their phone discrimination performance.<n>We find that S3Ms do not exhibit clear evidence of either CP effects in terms of phonological acquisition.
• Score: 13.643286736802414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates whether the Critical Period (CP) effects in human language acquisition are observed in self-supervised speech models (S3Ms). CP effects refer to greater difficulty in acquiring a second language (L2) with delayed L2 exposure onset, and greater retention of their first language (L1) with delayed L1 exposure offset. While previous work has studied these effects using textual language models, their presence in speech models remains underexplored despite the central role of spoken language in human language acquisition. We train S3Ms with varying L2 training onsets and L1 training offsets on child-directed speech and evaluate their phone discrimination performance. We find that S3Ms do not exhibit clear evidence of either CP effects in terms of phonological acquisition. Notably, models with delayed L2 exposure onset tend to perform better on L2 and delayed L1 exposure offset leads to L1 forgetting.

Related papers

• Language Models as Artificial Learners: Investigating Crosslinguistic Influence [11.168086425477467]
  We study the effect of varying the L1 language dominance and the L2 language proficiency.<n>Using cross-linguistic priming, we analyze how activating L1 structures impacts L2 processing.
  arXiv  Detail & Related papers  (2026-01-29T11:53:48Z)
• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases [22.048949559200935]
  This study evaluates Large Language Models' ability to simulate non-native-like English use observed in human second language (L2) learners.<n>In dialogue-based interviews, we prompt LLMs to mimic L2 English learners with specific L1s across seven languages.<n>Our analysis examines L1-driven linguistic biases, such as reference word usage and avoidance behaviors, using information-theoretic and distributional density measures.
  arXiv  Detail & Related papers  (2025-02-20T12:34:46Z)
• Investigating Critical Period Effects in Language Acquisition through Neural Language Models [70.6367059367609]
  Second language (L2) acquisition becomes harder after early childhood. ceasing exposure to a first language (L1) after this period (but not before) typically does not lead to substantial loss of L1 proficiency. It is unknown whether these CP effects result from innately determined brain maturation or as a stabilization of neural connections naturally induced by experience.
  arXiv  Detail & Related papers  (2024-07-27T19:17:10Z)
• Language Proficiency and F0 Entrainment: A Study of L2 English Imitation in Italian, French, and Slovak Speakers [48.3822861675732]
  This study explores F0 entrainment in second language (L2) English speech imitation during an Alternating Reading Task (ART) participants with Italian, French, and Slovak native languages imitated English utterances. Results indicate a nuanced relationship between L2 English proficiency and entrainment.
  arXiv  Detail & Related papers  (2024-04-16T10:10:19Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks [98.5311231450689]
  In-context learning (ICL) has played an essential role in utilizing large language models (LLMs) This study is the first work exploring ICL for speech classification tasks with textless speech LM.
  arXiv  Detail & Related papers  (2023-10-19T05:31:45Z)
• Second Language Acquisition of Neural Language Models [17.356128991925576]
  This work sheds light on the second language (L2) acquisition of neural language models (LMs) We trained bilingual LMs with a scenario similar to human L2 acquisition and analyzed their cross-lingual transfer from linguistic perspectives.
  arXiv  Detail & Related papers  (2023-06-05T14:32:41Z)
• SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT [0.0]
  Cross-linguistic transfer is the influence of linguistic structure of a speaker's native language on the successful acquisition of a foreign language. We find that NLP literature has not given enough attention to the phenomenon of negative transfer. Our findings call for further research using our novel Transformer-based SLA models.
  arXiv  Detail & Related papers  (2023-05-31T06:22:07Z)
• Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition [58.849768879796905]
  We propose Adapt-and-Adjust (A2), a transformer-based multi-task learning framework for end-to-end multilingual speech recognition. The A2 framework overcomes the long-tail problem via three techniques: (1) exploiting a pretrained multilingual language model (mBERT) to improve the performance of low-resource languages; (2) proposing dual adapters consisting of both language-specific and language-agnostic adaptation with minimal additional parameters; and (3) overcoming the class imbalance, either by imposing class priors in the loss during training or adjusting the logits of the softmax output during inference.
  arXiv  Detail & Related papers  (2020-12-03T03:46:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.