Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering

• URL: http://arxiv.org/abs/2606.11836v2
• Date: Thu, 11 Jun 2026 17:42:41 GMT
• Title: Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering
• Abstract summary: This paper presents a data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means.<n>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.
• Score: 22.824815645805117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 before fine-tuning and 0.19%/0.79% absolute (3.36%/4.62% relative) after fine-tuning with only 3 epochs. Similar WER reductions of 2.86%/5.02% absolute (59.21%/55.29% relative) were observed against magnitudebased pruning on Whisper-large-v3 at 10% sparsity, all with no significant WER increase relative to the uncompressed baseline.

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