基于特征融合与时延神经网络的说话人确认系统OA

In response to the problem that single-feature parameters in speaker recognition often fail to fully capture the unique infor-mation of the speaker and the decline in recognition rates in traditional voiceprint recognition systems under complex noisy environ-ments,a speaker confirmation system combining feature fusion and time-delay neural networks has been proposed.The system first analyzes MFCC(mel-frequency cepstral coefficient)and GFCC(gammatone frequency cepstral coefficients)features.MFCC fea-tures are widely used due to their effective representation of the Mel-frequency characteristics of speech signals,while GFCC fea-tures demonstrate better robustness in noisy environments as they mimic the auditory properties of the human cochlea.Subsequently,these two types of feature parameters and their dynamic characteristics are combined to form a high-dimensional feature set.To im-prove the system's processing efficiency,Principal Component Analysis(PCA)technology is used to reduce the dimensionality of the combined feature parameter set.Then,the k-means clustering algorithm is applied to further group these features,thereby con-structing a new type of hybrid feature parameter.Finally,this hybrid feature parameter is applied to the ECAPA-TDNN,a time-de-lay neural network with a channel attention mechanism,for both training and testing.Experimental results show that the proposed method has improved the recognition rate by 27.21%in the specified-5 dB noisy environment compared to traditional single fea-tures,demonstrating better recognition performance.