Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering
arXiv:2606.11836v1 Announce Type: cross Abstract: This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means. More fine-grained, mixed sparsity pruning by layer-level varying number of parameter clusters is also explored. Experiments conducted on the LibriSpeech dataset suggest that when operating with pruning sparsity of 50% on HuBERT-large, consistent WER reductions of 27.73%/18.61% absolute (34.37%/21.91% relative) over the magnitude-based pruning were obtained on the test-clean and test-other subsets be
The proliferation of increasingly large foundation models has made efficient compression techniques critical for practical deployment and resource optimization.
This data-free and training-free compression method offers significant efficiency gains for large speech models without requiring extensive retraining or data, accelerating their real-world adoption.
The barrier to deploying high-performance but resource-intensive speech foundation models is lowered, making advanced AI capabilities more accessible and efficient for various applications.
- · AI developers
- · Cloud computing providers
- · Edge AI hardware manufacturers
- · Speech technology companies
- · Less efficient compression methods
More sophisticated speech AI becomes deployable on a wider range of devices and with reduced operational costs.
This could accelerate the development of real-time, on-device AI assistants, translation tools, and advanced voice interfaces.
Increased efficiency in AI model deployment may contribute to broader economic productivity gains and innovation in AI-powered services.
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Read at arXiv cs.AI