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arXiv · 2506.18843

USAD: Universal Speech and Audio Representation via Distillation

Heng-Jui Chang·Saurabhchand Bhati·James Glass·Alexander H. Liu
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Abstract

Self-supervised learning (SSL) has revolutionized audio representations, yet models often remain domain-specific, focusing on either speech or non-speech tasks. In this work, we present Universal Speech and Audio Distillation (USAD), a unified approach to audio representation learning that integrates diverse audio types - speech, sound, and music - into a single model. USAD employs efficient layer-to-layer distillation from domain-specific SSL models to train a student on a comprehensive audio dataset. USAD offers competitive performance across various benchmarks and datasets, including frame and instance-level speech processing tasks, audio tagging, and sound classification, achieving near state-of-the-art results with a single encoder on SUPERB and HEAR benchmarks.

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Heng-Jui Chang, Saurabhchand Bhati, James Glass, Alexander H. Liu. 2025-06-23. USAD: Universal Speech and Audio Representation via Distillation. https://arxiv.org/abs/2506.18843

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