Combining knowledge distillation with l0 or low-rank pruning improves compressed RNN-T ASR, and joint pruning with fine-tuning gives 8.9% and 13.4% relative WER gains over baseline.
Data2Vec: A general framework for self- supervised learning in speech, vision and language,
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Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models
Combining knowledge distillation with l0 or low-rank pruning improves compressed RNN-T ASR, and joint pruning with fine-tuning gives 8.9% and 13.4% relative WER gains over baseline.