Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding

by   Yingting Li, et al.

Fine-tuning is widely used as the default algorithm for transfer learning from pre-trained models. Parameter inefficiency can however arise when, during transfer learning, all the parameters of a large pre-trained model need to be updated for individual downstream tasks. As the number of parameters grows, fine-tuning is prone to overfitting and catastrophic forgetting. In addition, full fine-tuning can become prohibitively expensive when the model is used for many tasks. To mitigate this issue, parameter-efficient transfer learning algorithms, such as adapters and prefix tuning, have been proposed as a way to introduce a few trainable parameters that can be plugged into large pre-trained language models such as BERT, and HuBERT. In this paper, we introduce the Speech UndeRstanding Evaluation (SURE) benchmark for parameter-efficient learning for various speech-processing tasks. Additionally, we introduce a new adapter, ConvAdapter, based on 1D convolution. We show that ConvAdapter outperforms the standard adapters while showing comparable performance against prefix tuning and LoRA with only 0.94 task in SURE. We further explore the effectiveness of parameter efficient transfer learning for speech synthesis task such as Text-to-Speech (TTS).


page 1

page 2

page 3

page 4


Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters

Recently, the pre-trained Transformer models have received a rising inte...

Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning

As the size of the pre-trained language model (PLM) continues to increas...

Parameter-Efficient Transfer Learning for NLP

Fine-tuning large pre-trained models is an effective transfer mechanism ...

ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning for Action Recognition

Capitalizing on large pre-trained models for various downstream tasks of...

Towards a Unified View of Parameter-Efficient Transfer Learning

Fine-tuning large pre-trained language models on downstream tasks has be...

VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

Recently, fine-tuning language models pre-trained on large text corpora ...

Parameter Efficient Transfer Learning for Various Speech Processing Tasks

Fine-tuning of self-supervised models is a powerful transfer learning me...

Code Repositories


Codes and datasets for our ICASSP2023 paper, Evaluating parameter-efficient transfer learning approaches on SURE benchmark for speech understanding

view repo

Please sign up or login with your details

Forgot password? Click here to reset