微型电脑应用2026，Vol.42Issue(4)：6-8,3.
深度卷积神经网络在多语种说话人识别中的应用
Application of Deep Convolutional Neural Network in Multilingual Speaker Recognition
摘要
Abstract
Aiming at the issue of insufficient robustness of multilingual speaker recognition in complex noisy environments,this paper proposes a recognition method based on multi-resolution convolutional neural network(MRCNN).By designing the multi-layer CNN architecture with different convolution kernel sizes,the features of different frequency ranges in speech signals are captured and the robustness problem in complex noise environments is solved.The experimental results on the Aurora2 multi-condition noise set show that the average recognition accuracy of the proposed method reaches 61.2372％,which is 3.4500％higher than that of the deep belief network(DBN)and 0.5789％higher than that of the traditional CNN.The pro-posed method provides an efficient and robust solution for multilingual speaker recognition,which has broad application pros-pects in complex speech environments.关键词
深度卷积神经网络/多分辨率卷积神经网络/多语种说话人识别/噪声鲁棒性Key words
deep convolutional neural network/multi-resolution convolutional neural network/multilingual speaker recogni-tion/noise robustness分类
信息技术与安全科学引用本文复制引用
孙杰,杨治学..深度卷积神经网络在多语种说话人识别中的应用[J].微型电脑应用,2026,42(4):6-8,3.基金项目
新疆维吾尔自治区科技厅自然科学面上项目(2022D01C03) （2022D01C03）
