Emergence of Phones, Syllables, and Words in Audiovisual Learning

Core Concepts

The author explores the idea of the Latent Language Hypothesis (LLH) in audiovisual learning, investigating how linguistic units like phones, syllables, and words may emerge as side-products without explicit learning targets.

Abstract

Decades of research have focused on language acquisition challenges for infants. The study delves into the latent language hypothesis (LLH), proposing that linguistic knowledge can emerge from audiovisual learning. Computational models demonstrate successful speech comprehension skills without prior linguistic knowledge. The study investigates whether phonetic, syllabic, and lexical representations can emerge from audiovisual learning processes. Results suggest that cross-modal learning may facilitate early language development beyond word association. Various neural network models are explored to understand the emergence of linguistic structures at different levels. The study highlights the potential for meaning-driven predictive learning to bootstrap language representation without explicit unit segmentation.

Stats

"In this study, we formulate this idea as the so-called latent language hypothesis (LLH), connecting linguistic representation learning to general predictive processing within and across sensory modalities."
"We investigate whether the latent representations learned by the networks reflect phonetic, syllabic, or lexical structure of input speech by utilizing an array of complementary evaluation metrics related to linguistic selectivity and temporal characteristics of the representations."

Quotes

"The finding is also robust against variations in model architecture or characteristics of model training and testing data."
"The results suggest that cross-modal and cross-situational learning may assist in early language development much beyond just enabling association of acoustic word forms to their referential meanings."

Key Insights Distilled From

Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation

by Khaz... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2109.14200.pdf

Deeper Inquiries

How does cross-modal learning impact traditional views on early language acquisition?

Cross-modal learning challenges traditional views on early language acquisition by suggesting that linguistic knowledge could emerge as a latent representational system through predictive processing across sensory modalities. This idea deviates from the conventional step-by-step approach to language learning, where infants are thought to sequentially tackle tasks like phoneme recognition, word segmentation, and meaning acquisition. Instead, cross-modal learning proposes a unified goal of minimizing predictive uncertainty in a multisensory environment. By connecting auditory speech with visual referents without explicit focus on linguistic units, this approach suggests that language skills may develop more holistically than previously believed.

What are potential limitations or criticisms of the latent language hypothesis proposed?

One potential limitation of the latent language hypothesis is its reliance on computational models rather than direct empirical evidence from human infants. While these models can simulate aspects of audiovisual learning and representation emergence, they may not fully capture the complexity and nuances of human language acquisition processes. Additionally, critics might argue that focusing solely on predictive processing overlooks other important factors influencing early language development, such as social interaction, cultural context, and individual differences among learners.

How might understanding neural predictions enhance our comprehension of human language acquisition?

Understanding neural predictions can provide valuable insights into how humans acquire language at different levels of granularity—phonetic, syllabic, and lexical structures. By studying how neural networks learn to associate auditory speech with visual input in an unsupervised manner, researchers can uncover underlying mechanisms involved in early language acquisition. This knowledge can help refine existing theories of linguistic representation learning and shed light on the cognitive processes guiding infant communication development.