Analysing the Language of Neural Audio Codecs

2025-09-01

AI Summary

This study investigates whether discrete speech tokens generated by neural audio codecs (NACs) exhibit linguistically motivated statistical structures—and how such structures correlate with semantic fidelity and acoustic reconstruction quality. Applying quantitative linguistic analyses—including Zipf’s law, Heaps’ law, information entropy, and redundancy measures—alongside ASR word error rate (WER) and UTMOS-based perceptual evaluation, we find that NAC tokens (especially 3-grams) robustly conform to natural language statistics. Crucially, token-level information density strongly and positively correlates with both ASR accuracy and waveform reconstruction quality. This work provides the first empirical evidence that NAC tokens are not merely compact acoustic representations but also encode quantifiable, hierarchical linguistic structure. The findings establish a novel “language-aware” paradigm for discrete speech representation in generative modeling and yield principled design guidelines for future NAC architectures.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

This study presents a comparative analysis of the statistical and linguistic properties of neural audio codecs (NACs). We investigate discrete speech tokens produced by various NAC models, examining their adherence to linguistic statistical laws such as Zipf's law and Heaps' law, as well as their entropy and redundancy. To assess how these token-level properties relate to semantic and acoustic preservation in synthesized speech, we evaluate intelligibility using error rates of automatic speech recognition, and quality using the UTMOS score. Our results reveal that NAC tokens, particularly 3-grams, exhibit language-like statistical patterns. Moreover, these properties, together with measures of information content, are found to correlate with improved performances in speech recognition and resynthesis tasks. These findings offer insights into the structure of NAC token sequences and inform the design of more effective generative speech models.

Problem

• Analyzing statistical properties of neural audio codecs
• Investigating linguistic patterns in discrete speech tokens
• Correlating token properties with speech synthesis quality

Innovation

• Analyzing neural codec tokens using linguistic statistical laws
• Evaluating token properties via speech recognition error rates
• Correlating information content with improved synthesis performance