This study focuses on Parameter-Efficient Fine-Tuning (PEFT) in speech processing, exploring optimal placement and merging strategies, as well as the use of ensemble learning. The results reveal that Differentiable Architecture…
Continue readingThe article discusses the use of Parameter-Efficient Fine-Tuning (PEFT) in speech processing. The authors conducted experiments to compare different PEFT methods and their layer-wise placement using Differentiable Architecture Search (DARTS)….
Continue readingThe article discusses the development of Fast-HuBERT, an efficient training framework for self-supervised learning (SSL) models for speech recognition. The authors address the high computational cost of existing SSL models,…
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