Zhiri Li

With the rapid development of synthetic speech and deepfake technology, fake speech poses a severe challenge to voice authentication systems. Traditional detection methods generally rely on manual feature extraction, facing problems such as limited feature expression ability and insufficient cross-scenario generalization performance. To this end, this paper proposes an improved ResNet network based on a Convolutional Block Attention Module (CBAM) for end-to-end fake speech detection. This method introduces channel attention and spatial attention mechanisms into the ResNet network structure to enhance the model’s attention to the temporal characteristics of speech, thereby improving the ability to distinguish between real and fake speech. The proposed model adopts an end-to-end training strategy, directly processes the original spectrogram input, uses the residual structure to alleviate the gradient vanishing problem in the deep network, and enhances the collaborative expression ability of local details and global context through the CBAM module. The experiment is conducted on the ASVspoof2019 LA dataset, and the equal error rate (EER) is used as the main evaluation indicator. The experimental results show that compared with traditional deepfake speech detection methods, the proposed model achieves better performance in indicators such as EER, verifying the effectiveness of the CBAM attention mechanism in forged speech detection.

The score-based diffusion model has made significant progress in the field of computer vision, surpassing the performance of generative models, such as variational autoencoders, and has been extended to applications such as speech enhancement and recognition. This paper proposes a U-Net architecture using a score-based diffusion model and an efficient multi-scale attention mechanism (EMA) for the speech enhancement task. The model leverages the symmetric structure of U-Net to extract speech features and captures contextual information and local details across different scales using the EMA mechanism, improving speech quality in noisy environments. We evaluate the method on the VoiceBank-DEMAND (VB-DMD) dataset and the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus–TUT Sound Events 2017 (TIMIT-TUT) dataset. The experimental results show that the proposed model performed well in terms of speech quality perception (PESQ), extended short-time objective intelligibility (ESTOI), and scale-invariant signal-to-distortion ratio (SI-SDR). Especially when processing out-of-dataset noisy speech, the proposed method achieved excellent speech enhancement results compared to other methods, demonstrating the model’s strong generalization capability. We also conducted an ablation study on the SDE solver and the EMA mechanism, and the results show that the reverse diffusion method outperformed the Euler–Maruyama method, and the EMA strategy could improve the model performance. The results demonstrate the effectiveness of these two techniques in our system. Nevertheless, since the model is specifically designed for Gaussian noise, its performance under non-Gaussian or complex noise conditions may be limited.