关键词: 关键词: 声纹库, 时延神经网络, 多尺度特征提取, 频率通道注意力, 特征注意力融合

Abstract: Abstract: In order to solve the problem that the accuracy of voicing recognition is easily affected by external factors, a voicing recognition algorithm based on multi-scale frequency channel attention fusion delay neural network model is proposed. Based on the ECAPA-TDNN model, the model has three improvements, including: A multi-scale frequency channel attention front end is added to obtain high-resolution feature representation from discourse, a multi-scale channel attention module is added to combine local and global features to fuse multi-scale information, and a feature attention fusion module is embedded to weigh multi-scale fusion features. With these improvements, the model makes better use of multi-scale time-frequency information and improve the recognition ability. In order to prove that the above improvement points can effectively improve the accuracy and reliability of voiceprint recognition, based on the public telephone voice data set, we designed correlation comparison experiments from the aspect of data enhancement, recognition effect comparison experiments from the aspect of feature extraction and ablation experiments from the model improvement part. The experimental results show that compared with ECAPA-TDNN model, the reduction rate of equal error rate and minimum detection cost function in MFCA-TDNN model is 5.9% and 7.9%, respectively. The lowest equal error rate can be reduced to 3.83%, and the smallest detection cost function can reach 0.2202.

Key words: Keywords: voiceprint library, delay neural network, multi-scale feature extraction, frequency channel attention, feature attention fusion