传感技术学报2026，Vol.39Issue(3)：561-570,10.DOI:10.3969/j.issn.1004-1699.2026.03.013
基于卷积神经网络与时延神经网络加权特征融合的语种识别
Language Recognition Based on Weighted Feature Fusion of Convolutional Neural Network and Time-Delay Neural Network
黄张衡 1龙华 2邵玉斌 2张洪波 3杨璞 3张靖南4
作者信息
- 1. 昆明理工大学信息工程与自动化学院,云南 昆明 650500||民航云南空管分局,云南 昆明 650200
- 2. 昆明理工大学信息工程与自动化学院,云南 昆明 650500
- 3. 民航云南空管分局,云南 昆明 650200
- 4. 云南民族大学教育学院,云南 昆明 650504
- 折叠
摘要
Abstract
Targeting at the low recognition rate of traditional Convolutional Neural Network(CNN)and Time-Delay Neural Network(TDNN)network,a new language recognition network model is proposed.To avoid the phenomenon of neuron death during training.The frequency domain attention mechanism is introduced on the basis of the traditional CNN network and in the output of the convolution layer,the value on the negative axis of the ReLU activation function is taken by an absolute value and multiplied by a minimum con-stant.Then,on the basis of ECAPA-TDNN network,a pooling layer is introduced after the first layer of convolution to remove redundant information in features.Finally,weighted fusion is performed between the constructed FCA-CNN network and NECAPA-TDNN network to form a fusion network,and the extracted speech features are used as the input of the fusion network for classification and recognition.The experimental results show that the average recognition accuracy of the spectrogram features in LibriVox data set is improved by 10.96%and 17.12%compared with CNN and TDNN,which verifies the effectiveness and recognition performance of the fusion network.关键词
语种识别/加权融合网络/神经网络分类/频域注意力Key words
language recognition/weighted fusion network/neural network classification/frequency domain attention引用本文复制引用
黄张衡,龙华,邵玉斌,张洪波,杨璞,张靖南..基于卷积神经网络与时延神经网络加权特征融合的语种识别[J].传感技术学报,2026,39(3):561-570,10.
