基于交叉注意力的声纹识别与语音欺骗检测集成方法

In the field of integrating voiceprint recognition and speech spoofing detection, existing back - end embedding fusion methods have some deficiencies and are difficult to fully explore the internal relationships between voiceprint recognition and spoofing detection tasks. This paper proposes an embedding fusion algorithm. By deeply analyzing the unique relationships between voiceprint embeddings and spoofing embeddings, the traditional cross - attention mechanism is improved to obtain comprehensive global feature interaction information. Moreover, in combination with the convolutional module, it can accurately capture the complex correlations between voiceprint embeddings and spoofing embeddings. To further optimize the integration effect, an embedding selective fusion module is designed to adaptively adjust the contribution weights and avoid the bias problems commonly existing in current integration methods.This algorithm takes a pair of registered speech and test speech as input data. Firstly, the voiceprint recognition subsystem and the speech spoofing detection subsystem are used to extract voiceprint embeddings and spoofing embeddings respectively. Then, an embedding fusion method based on cross - attention is applied to deeply fuse these two types of embeddings. Finally, the classification layer outputs the final result, ensuring the stability and reliability of the entire system operation. The experimental results on the ASVspoof2019 dataset show that the model proposed in this paper has achieved a significant performance improvement, which effectively verifies the effectiveness and superiority of the algorithm.