Research on Robustness of Voiceprint Recognition Technology

In the mobile Internet era, voiceprint recognition technology is an important means of identity authentication in social, shopping, online shop, financial transactions and other activities. With the popularity of smart phones, these network activities can be carried out at anytime and anywhere. Inevitably, there are a variety of environmental noise in the voice input process of voiceprint recognition. Improving the anti-noise and robustness of voiceprint recognition system is the key to the wide application of voiceprint recognition technology. This paper focuses on the key modules of voiceprint recognition system, such as endpoint detection, feature extraction, voiceprint modeling and matching. Mainstream technologies of each module are analyzed, and a robust voiceprint recognition method is proposed. In this method, the endpoint detection algorithm based on spectrum is used, and the Empirical Mode Decomposition (EMD Empirical Mode Decomposition) algorithm is used to reconstruct the spectrum, which can effectively realize voiceprint denoising. Using the superior performance of convolution neural network (CNN) model in the field of image, the voiceprint model is constructed to improve the recognition efficiency and realize the robustness of the voiceprint recognition system.