Prompting And Adapter Tuning For Self-supervised Encoder-decoder Speech Model

Chang Kai-wei, Chen Ming-hsin, Lin Yun-ping, Hsu Jing Neng, Huang Paul Kuo-ming, Huang Chien-yu, Li Shang-wen, Lee Hung-yi. Arxiv 2023

[Paper]    
Fine Tuning Pretraining Methods Prompting Training Techniques

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.

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