USAD: Universal Speech and Audio Representation via Distillation

• URL: http://arxiv.org/abs/2506.18843v1
• Date: Mon, 23 Jun 2025 17:02:00 GMT
• Title: USAD: Universal Speech and Audio Representation via Distillation
• Authors: Heng-Jui Chang, Saurabhchand Bhati, James Glass, Alexander H. Liu,
• Abstract summary: Universal Speech and Audio Distillation (USAD) is a unified approach to audio representation learning.<n>USAD integrates diverse audio types - speech, sound, and music - into a single model.
• Score: 56.91647396619358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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