Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning
- URL: http://arxiv.org/abs/2412.17456v1
- Date: Mon, 23 Dec 2024 10:23:47 GMT
- Title: Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning
- Authors: Xiaodan Chen, Alexandre Pitti, Mathias Quoy, Nancy F Chen,
- Abstract summary: We propose a small-sized generative neural network equipped with a continual learning mechanism.
Our model prioritizes interpretability and demonstrates the advantages of online learning.
- Score: 69.8008228833895
- License:
- Abstract: Understanding how infants perceive speech sounds and language structures is still an open problem. Previous research in artificial neural networks has mainly focused on large dataset-dependent generative models, aiming to replicate language-related phenomena such as ''perceptual narrowing''. In this paper, we propose a novel approach using a small-sized generative neural network equipped with a continual learning mechanism based on predictive coding for mono-and bilingual speech sound learning (referred to as language sound acquisition during ''critical period'') and a compositional optimization mechanism for generation where no learning is involved (later infancy sound imitation). Our model prioritizes interpretability and demonstrates the advantages of online learning: Unlike deep networks requiring substantial offline training, our model continuously updates with new data, making it adaptable and responsive to changing inputs. Through experiments, we demonstrate that if second language acquisition occurs during later infancy, the challenges associated with learning a foreign language after the critical period amplify, replicating the perceptual narrowing effect.
Related papers
- An iterated learning model of language change that mixes supervised and unsupervised learning [0.0]
The iterated learning model is an agent model which simulates the transmission of of language from generation to generation.
In each iteration, a language tutor exposes a na"ive pupil to a limited training set of utterances, each pairing a random meaning with the signal that conveys it.
The transmission bottleneck ensures that tutors must generalize beyond the training set that they experienced.
arXiv Detail & Related papers (2024-05-31T14:14:01Z) - SINC: Self-Supervised In-Context Learning for Vision-Language Tasks [64.44336003123102]
We propose a framework to enable in-context learning in large language models.
A meta-model can learn on self-supervised prompts consisting of tailored demonstrations.
Experiments show that SINC outperforms gradient-based methods in various vision-language tasks.
arXiv Detail & Related papers (2023-07-15T08:33:08Z) - Learning Cross-lingual Visual Speech Representations [108.68531445641769]
Cross-lingual self-supervised visual representation learning has been a growing research topic in the last few years.
We use the recently-proposed Raw Audio-Visual Speechs (RAVEn) framework to pre-train an audio-visual model with unlabelled data.
Our experiments show that: (1) multi-lingual models with more data outperform monolingual ones, but, when keeping the amount of data fixed, monolingual models tend to reach better performance.
arXiv Detail & Related papers (2023-03-14T17:05:08Z) - Communication Drives the Emergence of Language Universals in Neural
Agents: Evidence from the Word-order/Case-marking Trade-off [3.631024220680066]
We propose a new Neural-agent Language Learning and Communication framework (NeLLCom) where pairs of speaking and listening agents first learn a miniature language.
We succeed in replicating the trade-off with the new framework without hard-coding specific biases in the agents.
arXiv Detail & Related papers (2023-01-30T17:22:33Z) - Dependency-based Mixture Language Models [53.152011258252315]
We introduce the Dependency-based Mixture Language Models.
In detail, we first train neural language models with a novel dependency modeling objective.
We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention.
arXiv Detail & Related papers (2022-03-19T06:28:30Z) - Towards Language Modelling in the Speech Domain Using Sub-word
Linguistic Units [56.52704348773307]
We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes.
With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech.
We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
arXiv Detail & Related papers (2021-10-31T22:48:30Z) - Word Acquisition in Neural Language Models [0.38073142980733]
We investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words.
We find that the effects of concreteness, word length, and lexical class are pointedly different in children and language models.
arXiv Detail & Related papers (2021-10-05T23:26:16Z) - Towards Zero-shot Language Modeling [90.80124496312274]
We construct a neural model that is inductively biased towards learning human languages.
We infer this distribution from a sample of typologically diverse training languages.
We harness additional language-specific side information as distant supervision for held-out languages.
arXiv Detail & Related papers (2021-08-06T23:49:18Z) - Deep Sound Change: Deep and Iterative Learning, Convolutional Neural
Networks, and Language Change [0.0]
This paper proposes a framework for modeling sound change that combines deep learning and iterative learning.
It argues that several properties of sound change emerge from the proposed architecture.
arXiv Detail & Related papers (2020-11-10T23:49:09Z) - Generative Adversarial Phonology: Modeling unsupervised phonetic and
phonological learning with neural networks [0.0]
Training deep neural networks on well-understood dependencies in speech data can provide new insights into how they learn internal representations.
This paper argues that acquisition of speech can be modeled as a dependency between random space and generated speech data in the Generative Adversarial Network architecture.
We propose a methodology to uncover the network's internal representations that correspond to phonetic and phonological properties.
arXiv Detail & Related papers (2020-06-06T20:31:23Z)
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.