On the Learnability of Syntax from Raw Speech with Autoregressive Predictive Coding

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Autoregressive Predictive Coding (APC) research investigates whether syntactic generalization can emerge directly from raw acoustic input, mirroring early human language acquisition. The study trained APC, a simple prediction-based self-supervised speech model, on both child-directed speech and audiobook speech. Models were evaluated using a minimal-pair benchmark designed for elementary syntactic phenomena. Results indicate that APC partially generalizes word-order regularities when trained to predict near-future frames. However, the model failed to generalize agreement phenomena, suggesting that predictive learning from acoustic signals alone is insufficient for full syntactic acquisition. Distinct learning dynamics across word-order phenomena also imply that some observed improvements may stem from shallow statistical regularities rather than genuine syntactic generalization.

Key takeaway

For AI Scientists developing speech models for language acquisition, this research indicates that while Autoregressive Predictive Coding can capture some word-order regularities from raw acoustic input, it struggles with agreement phenomena. You should consider augmenting predictive learning with additional modalities or architectural complexities to achieve more robust syntactic generalization, moving beyond shallow statistical patterns. This is crucial for models aiming to replicate human-like language learning capabilities.

Key insights

Predictive learning from raw speech partially captures word-order but not agreement, suggesting limitations for syntax acquisition.

Principles

• APC can learn word-order from raw speech.
• Acoustic signals alone may not suffice for full syntax.
• Shallow regularities can mimic syntactic generalization.

Method

Train Autoregressive Predictive Coding (APC) on raw speech (child-directed, audiobooks) to predict near-future frames, then evaluate on minimal-pair syntactic benchmarks.

In practice

• Evaluate speech models on minimal-pair syntax.
• Distinguish deep syntax from shallow regularities.
• Consider multimodal input for full syntax.

Topics

• Autoregressive Predictive Coding
• Speech Models
• Syntax Acquisition
• Self-supervised Learning
• Word Order
• Agreement Phenomena

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.