Results from experiments show that DIET outperforms fine-tuned BERT and has the added
benefit of being six times faster to train.
• Chang et al. [59] investigated the effectiveness of prompt tuning on the GSLM architecture
and showcased its competitiveness on various SLU tasks, such as KS, IC, and SF. Impressively,
this approach achieves comparable results with fewer trainable parameters than full fine-
tuning. Despite being a popular and effective technique in numerous NLP tasks, prompt
tuning has not received much attention in the speech community. Additionally, other
researchers have pursued a different path by utilizing pre-trained wav2vec2.0 and different
adapters [315] to attain state-of-the-art outcomes.
AReviewofDeepLearningTechniquesforSpeechProcessing