  Prompting and adapter tuning have emerged as efficient alternatives to
fine-tuning (FT) methods. However, existing studies on speech prompting focused
on classification tasks and failed on more complex sequence generation tasks.
Besides, adapter tuning is primarily applied with a focus on encoder-only
self-supervised models. Our experiments show that prompting on Wav2Seq, a
self-supervised encoder-decoder model, surpasses previous works in sequence
generation tasks. It achieves a remarkable 53% relative improvement in word
error rate for ASR and a 27% in F1 score for slot filling. Additionally,
prompting competes with the FT method in the low-resource scenario. Moreover,
we show the transferability of prompting and adapter tuning on Wav2Seq in
cross-lingual ASR. When limited trainable parameters are involved, prompting
and adapter tuning consistently outperform conventional FT across 7 languages.
Notably, in the low-resource scenario, prompting consistently outperforms
adapter tuning.
