计算机应用与软件2024，Vol.41Issue(8)：298-302,5.DOI:10.3969/j.issn.1000-386x.2024.08.043
融合注意力机制的ResNeXt语音欺骗检测模型
SPEECH ANTI-SPOOFING MODEL BASED ON RESNEXT WITH ATTENTION
摘要
Abstract
Aimed at the problem that residual neural network has too many hyperparameters in speech deception detection,and the high-frequency features are not prominent enough,a ResNeXt-Attention network(RA-Net)fused with attention mechanism is proposed.RA-Net used residuals combined with grouped convolution,replaced large convolution kernels with a set of small convolution kernels,and used MFM(max feature map)as a new splicing method.The added attention mechanism reduced the attention to edge information by learning the original feature information.Experiments on the ASVspoof2019 data set show that compared with the baseline Gaussian mixture model(GMM),the equal error rate(EER)of RA-Net is reduced by 4.72 percentage points and 6.23 percentage points.And the EER is reduced by 0.69 percentage points and 0.89 percentage points compared with the residual network(ResNet).The validity of the model is confirmed.关键词
语音欺骗检测/ResNeXt/MFM/注意力机制/RA-NetKey words
Speech anti-spoofing/ResNeXt/MFM/Attention mechanism/RA-Net分类
信息技术与安全科学引用本文复制引用
张旺,杨乘,罗娅娅..融合注意力机制的ResNeXt语音欺骗检测模型[J].计算机应用与软件,2024,41(8):298-302,5.基金项目
国家自然科学基金项目(62062025,61662010) （62062025,61662010）
贵州省科学技术基金重点项目(黔科合基础[2019]1432). （黔科合基础[2019]1432）
