CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models
This work addresses efficient adaptation for speech tasks, offering a domain-specific improvement for emotion and speaker recognition.
The paper tackles the problem of inefficient fine-tuning for self-supervised speech models by proposing CHAPTER, a method that applies CNN adapters to the feature extractor, achieving better performance with fewer than 5% of parameters tuned per task, such as improving SID accuracy from 87.71 to 91.56 and ER accuracy by 5%.
Self-supervised learning (SSL) is a powerful technique for learning representations from unlabeled data. Transformer based models such as HuBERT, which consist a feature extractor and transformer layers, are leading the field in the speech domain. SSL models are fine-tuned on a wide range of downstream tasks, which involves re-training the majority of the model for each task. Previous studies have introduced applying adapters, which are small lightweight modules commonly used in Natural Language Processing (NLP) to adapt pre-trained models to new tasks. However, such efficient tuning techniques only provide adaptation at the transformer layer, but failed to perform adaptation at the feature extractor. In this paper, we propose CHAPTER, an efficient tuning method specifically designed for SSL speech model, by applying CNN adapters at the feature extractor. Using this method, we can only fine-tune fewer than 5% of parameters per task compared to fully fine-tuning and achieve better and more stable performance. We empirically found that adding CNN adapters to the feature extractor can help the adaptation on emotion and speaker tasks. For instance, the accuracy of SID is improved from 87.71 to 91.56, and the accuracy of ER is improved by 5%.