Biometric speech recognition systems are often subject to various spoofing attacks,the most common of which are speech synthesis and speech conversion attacks.These spoofing attacks can cause the biometric speech reco...Biometric speech recognition systems are often subject to various spoofing attacks,the most common of which are speech synthesis and speech conversion attacks.These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks,which can compromise the security of this system.Researchers have made many efforts to address this problem,and the existing studies have used the physical features of speech to identify spoofing attacks.However,recent studies have shown that speech contains a large number of physiological features related to the human face.For example,we can determine the speaker's gender,age,mouth shape,and other information by voice.Inspired by the above researches,we propose a spoofing attack recognition method based on physiological-physical features fusion.This method involves feature extraction,a densely connected convolutional neural network with squeeze and excitation block(SE-DenseNet),and feature fusion strategies.We first extract physiological features in audio from a pretrained convolutional network.Then we use SE-DenseNet to extract physical features.Such a dense connection pattern has high parameter efficiency,and squeeze and excitation blocks can enhance the transmission of the feature.Finally,we integrate the two features into the classification network to identify the spoofing attacks.Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection.In the logical access scenario,our model improves the tandem decision cost function and equal error rate scores by 5%and 7%,respectively,compared to existing methods.展开更多
文摘X射线衍射(XRD)图谱数据的采集和分析是新材料开发周期中必不可少的步骤之一,常规实验表征很难实现大批量的测试和快速鉴别.文章基于DenseNet设计了一个衍射图空间群识别的神经网络模型SE-DenseNet.SE-Dense Net在简化了网络结构的同时,通过增加注意力机制(Squeeze and Excitation,SE),并采用新的激活函数来提高网络模型的性能.研究表明,在具有32337个样本包含20类空间群的数据集上,SE-Dense Net的准确率为81.73%,较基础对照模型提高了4.9%.研究发现,尽管数据集的不平衡性是限制神经网络模型预测准确度的主要原因之一,但SE-DenseNet的性能足以在短时间对大量衍射图数据产生准确的预测,并提供有意义的参考.
基金supported by Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(HNTS2022035)the National Natural Science Foundation of China(Grant Nos.62036010 and 61972362)Young Backbone Teachers in Henan Province(22020GGJS014).
文摘Biometric speech recognition systems are often subject to various spoofing attacks,the most common of which are speech synthesis and speech conversion attacks.These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks,which can compromise the security of this system.Researchers have made many efforts to address this problem,and the existing studies have used the physical features of speech to identify spoofing attacks.However,recent studies have shown that speech contains a large number of physiological features related to the human face.For example,we can determine the speaker's gender,age,mouth shape,and other information by voice.Inspired by the above researches,we propose a spoofing attack recognition method based on physiological-physical features fusion.This method involves feature extraction,a densely connected convolutional neural network with squeeze and excitation block(SE-DenseNet),and feature fusion strategies.We first extract physiological features in audio from a pretrained convolutional network.Then we use SE-DenseNet to extract physical features.Such a dense connection pattern has high parameter efficiency,and squeeze and excitation blocks can enhance the transmission of the feature.Finally,we integrate the two features into the classification network to identify the spoofing attacks.Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection.In the logical access scenario,our model improves the tandem decision cost function and equal error rate scores by 5%and 7%,respectively,compared to existing methods.
