The NPU-ASLP System for Deepfake Algorithm Recognition in ADD 2023 Challenge

This paper describes our NPU-ASLP system for the Deepfake Algorithm Recognition (AR) task in the Audio Deepfake Detection 2023 Challenge. This task is an open-set classification problem focusing on identifying the specific algorithms used to create the deepfake speech utterances. In this task, we introduce a deepfake AR system with contributions in data augmentation, model architecture, fine-tuning strategy, and model ensemble. We first generate training data by applying various data augmentation techniques to the deepfake speech. We then utilize ResNet101 and a long-term temporal-frequency transformer module to better capture audio context dependencies. Moreover, we employ pre-trained WavLM for better feature extraction. Additionally, our content-invariant fine-tuning strategy improves performance. Finally, model ensemble with different representation combinations further enhances performance. Experiments show that our system achieves an F1-score of 0.7355 on the evaluation set, and ranks fourth in the challenge.