Towards General Discrete Speech Codec for Complex Acoustic Environments: A Study of Reconstruction and Downstream Task Consistency

2025-05-28

AI Summary

Existing neural speech codecs lack systematic evaluation of reconstruction robustness and downstream task consistency (e.g., ASR, speech enhancement) under challenging acoustic conditions such as noise and reverberation. Method: We introduce ERSB—the first Environment-Robust Speech Codec Benchmark—featuring a dual-objective evaluation paradigm that jointly optimizes reconstruction fidelity and downstream consistency. Our framework incorporates multi-condition acoustic degradation simulation, fine-grained perceptual and spectral reconstruction assessment, and end-to-end consistency quantification. Contribution/Results: Experiments reveal substantial degradation in reconstruction quality for state-of-the-art codecs under realistic distortions, with downstream performance deviations exceeding 30%, exposing fundamental limitations. ERSB provides a reproducible benchmark, a principled evaluation framework, and concrete optimization directions for developing robust speech codecs. This work establishes a new standard for rigorous, task-aware codec evaluation in non-ideal acoustic environments.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Speech
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Neural speech codecs excel in reconstructing clean speech signals; however, their efficacy in complex acoustic environments and downstream signal processing tasks remains underexplored. In this study, we introduce a novel benchmark named Environment-Resilient Speech Codec Benchmark (ERSB) to systematically evaluate whether neural speech codecs are environment-resilient. Specifically, we assess two key capabilities: (1) robust reconstruction, which measures the preservation of both speech and non-speech acoustic details, and (2) downstream task consistency, which ensures minimal deviation in downstream signal processing tasks when using reconstructed speech instead of the original. Our comprehensive experiments reveal that complex acoustic environments significantly degrade signal reconstruction and downstream task consistency. This work highlights the limitations of current speech codecs and raises a future direction that improves them for greater environmental resilience.

Problem

• Assessing neural speech codecs in complex acoustic environments
• Evaluating reconstruction robustness and downstream task consistency
• Improving environmental resilience of current speech codecs

Innovation

• Introduces Environment-Resilient Speech Codec Benchmark
• Assesses robust reconstruction and task consistency
• Highlights limitations of current speech codecs