arXiv:2605.23954v1 Announce Type: new Abstract: Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing robustness methods primarily rely on waveform-level acoustic enhancement, answer-level supervision, or the internal suppression of noise representations. To address these issues, we propose echodistill, an alignment-based noisy-to-clean self-distillation framework. Echodistill leverages a frozen clean-audio teacher to provide semantic references for an inference-time noisy-audio student. Specificall
The proliferation of AI applications in real-world, noisy environments necessitates robust solutions for performance, making 'EchoDistill' a timely development.
This research is crucial because it addresses a fundamental vulnerability in Audio LLMs, enabling more reliable and effective deployment in practical, uncontrolled settings.
Current methods for robustifying Audio LLMs are being augmented by a self-distillation framework that provides semantic references from clean audio, potentially improving real-world performance significantly.
- · AI developers
- · voice assistant providers
- · AI ethics and safety researchers
- · speech-to-text industry
- · developers relying solely on waveform-level robustness
- · companies with less robust ALLM offerings
Improved performance and reliability of Audio LLMs in noisy real-world environments.
Accelerated adoption and integration of voice-controlled AI systems across various industries.
Enhanced trust in AI systems leading to a broader array of applications that were previously impractical due to noise sensitivity.
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Read at arXiv cs.CL