Research on Lightweight Voiceprint Recognition Models

The development of lightweight voiceprint recognition models is aimed at addressing challenges encountered when deploying voiceprint recognition systems on resource-constrained devices. Despite the impressive performance of complex neural network models in certain tasks, they often require substantial storage space and computational resources, making them difficult to run on lightweight devices. Research on lightweight voiceprint recognition models primarily focuses on reducing the model’s size and computational requirements to accommodate the limited storage space and computational resources of mobile devices and embedded systems. This contributes to the more widespread deployment of voiceprint recognition technology in practical applications, enhancing its practicality. This article, based on depthwise separable convolution, designs a lightweight neural network that effectively reduces the complexity of the model.