计算机与数字工程2023，Vol.51Issue(12)：2827-2830,4.DOI:10.3969/j.issn.1672-9722.2023.12.011
基于改进时延神经网络的说话人识别方法
Speaker Recognition Method Based on Improved Time Delay Neural Network
胡贵超1
作者信息
- 1. 南京理工大学计算机科学与工程学院 南京 210018
- 折叠
摘要
Abstract
An improved time delay neural network(TDNN)speaker recognition method is proposed to improve the accuracy of speaker recognition.Firstly,the features of audio are trained through TDNN network to obtain the feature expression of some speakers.Then it is processed simultaneously by the added quantization and counting operators(QCO).QCO can make full use of the low-level texture features of audio to obtain the detailed information of features.The experimental results show that the improved time-delay neural network can obtain more information from network training in a relatively small amount of data,it has obvious ad-vantages in the network with a small number of training sets.When the amount of data is further increased,the effect is more obvi-ous.The training adds the texture statistical method to extract the detailed features of the structure,which makes the speaker recog-nition performance better.关键词
说话人识别/时延神经网络/量化和计数算子/qco-vectorKey words
speaker recognition/delay neural networks/quantization and counting operators/qco-vector分类
数理科学引用本文复制引用
胡贵超..基于改进时延神经网络的说话人识别方法[J].计算机与数字工程,2023,51(12):2827-2830,4.
