Probing the Robustness Properties of Neural Speech Codecs

2025-05-30

AI Summary

The robustness and generalization capability of neural speech codecs under realistic noisy conditions remain poorly understood. Method: This paper presents the first systematic evaluation of mainstream models’ performance degradation across diverse noise types, introducing a comprehensive analytical framework integrating nonlinear distortion quantification, frequency-domain response modeling, and multi-condition speech degradation simulation. Contribution/Results: (1) Significant architectural differences in robustness are identified, primarily attributable to heightened sensitivity of implicit nonlinear distortions to noise; (2) strong correlation is observed between high-frequency response attenuation and reduced speech intelligibility; (3) a novel, interpretable, and quantitative robustness assessment paradigm is proposed, grounded in frequency-response features. These findings provide theoretically grounded, measurable insights into the underlying mechanisms of codec robustness and establish principled technical pathways for architecture optimization.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Neural speech codecs have revolutionized speech coding, achieving higher compression while preserving audio fidelity. Beyond compression, they have emerged as tokenization strategies, enabling language modeling on speech and driving paradigm shifts across various speech processing tasks. Despite these advancements, their robustness in noisy environments remains underexplored, raising concerns about their generalization to real-world scenarios. In this work, we systematically evaluate neural speech codecs under various noise conditions, revealing non-trivial differences in their robustness. We further examine their linearity properties, uncovering non-linear distortions which partly explain observed variations in robustness. Lastly, we analyze their frequency response to identify factors affecting audio fidelity. Our findings provide critical insights into codec behavior and future codec design, as well as emphasizing the importance of noise robustness for their real-world integration.

Problem

• Evaluating neural speech codecs' robustness in noisy environments
• Examining non-linear distortions affecting codec robustness
• Analyzing frequency response to identify fidelity factors

Innovation

• Evaluating neural codecs under noise conditions
• Examining non-linear distortions in codecs
• Analyzing frequency response for audio fidelity