Small Sample Voiceprint Recognition Method Based on Deep Learning
摘要
When training Convolutional Neural NetWork(CNN) with small sample voiceprints as training set,the network cannot reach a good convergence state,which results in low recognition rate.So,this paper proposes a new voiceprint recognition method.The proposed method uses deep CNN to extract the rich and latent features of voiceprint,which improves the voiceprint recognition rate.In order to solve the problem that small sample cannot train the CNN,this paper proposes an image increasing algorithm based on the principle of convex lens imaging.At the same time,the Fast Batch Normalization (FBN) is introduced in the convolutional process,which improves the speed of the network convergence and shortens the training time.Select a TIMIT speech database containing voices of 630 speakers for training,validating and testing.Experimental results show that,compared with the GMM,GMM-UBM,and GMM-SVM algorithms,the proposed method improves the recognition rate by 7.3%,2.2%,and 2.8% and compared with the original network,the training time of the FBN-Alexnet network is reduced by 48.2%.It means that it is an effective method for voiceprint recognition of small samples.