LAUSR

As an essential biological feature of human beings, voiceprint is increasingly used in medical research and diagnosis, especially in identifying Parkinson’s Disease (PD). This paper proposes a Spectrogram Deep Convolutional Generative Adversarial Network (S-DCGAN) for sample augmentation to overcome the limited amount of existing patient voiceprint datasets and samples. S-DCGAN generates a high-resolution spectrogram by increasing network layers, adding the Spectral Normalization (SN) method, and combining feature matching strategy. The high-similarity and low-distortion spectrogram are selected in light of Structural Similarity Index (SSIM) values and Peak Signal to Noise Ratio (PSNR) to augment the samples. Fréchet Inception Distance (FID) and GAN-train result show the generalization ability of the generated data. We construct the ResNet50 model with a Global Average Pooling(GAP) layer to extract the voiceprint features and classify them effectively to improve recognition accuracy. The GAP suppresses the over-fitting problem and optimizes quickly. Finally, on the Sakar dataset, comparative experiments were conducted on different models and classification methods. Results show that the S-DCGAN-ResNet50 hybrid model can achieve the highest voiceprint recognition accuracy of 91.25% and specificity of 92.5%, which can distinguish between PD patients and healthy people more precisely compared with DCGAN-ResNet50. It augments the application environment of voiceprint recognition in the medical field and makes it universal in different datasets.