期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：1
语种：English
出版社：IAENG - International Association of Engineers
摘要：A voiceprint recognition method based on improved pooling strategy for convolutional neural networks (CNN) is proposed. For the voiceprint recognition, the voice signal is quantized and pre-emphasized, and then the processed voice information is framed and windowed, and the voiceprint information of the self-built digital voiceprint library is converted into a score map to construct a digital voiceprint database. Then an CNN based voiceprint recognition method by introducing an improved pooling method is proposed. The new pooling method is to square the activation value after the activation function, and assign the square number probability to realize the random pooling. This method can not only preserve the feature extraction of the maximum pooling method, but also absorb the advantage of random pooling. It retains the possibility of extracting hidden features, and effectively enhancing the generalization ability of the network. The traditional convolutional neural network LeNet-5 is modified by adopting the improved pooling method and introducing the Dropout idea so as to an improved LeNet-5 network. The voiceprint recognition experiments were performed by using the gray-scale digital spectrogramon in the self-built voiceprint database through the improved LeNet-5 network. The recognition experiments were performed on the VGG-16 network using the three-channel color digital spectrum map. An automatic voiceprint recognition system was built to detect voiceprints in real-time voice information. The simulation results verify the effectiveness of the proposed method.
