Spoofing speech detection algorithm based on joint feature and random forest

Jiaqi YU; Zhihua JIAN; Jia XU; Lin YOU; Yunlu WANG; Chao WU

doi:10.11959/j.issn.1000-0801.2022089

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Spoofing speech detection algorithm based on joint feature and random forest

Research and Development | 更新时间：2024-06-05

- Spoofing speech detection algorithm based on joint feature and random forest
- Telecommunications Science Vol. 38, Issue 6, Pages: 91-99(2022)
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院，浙江杭州 310018
  2. 杭州电子科技大学网络空间安全学院，浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022089
  CLC： TP391.42
- Published Online：2022-06，
  
  Published：20 June 2022
- 稿件说明：
移动端阅览
Jiaqi YU, Zhihua JIAN, Jia XU, et al. Spoofing speech detection algorithm based on joint feature and random forest[J]. Telecommunications science, 2022, 38(6): 91-99.
DOI：

Jiaqi YU, Zhihua JIAN, Jia XU, et al. Spoofing speech detection algorithm based on joint feature and random forest[J]. Telecommunications science, 2022, 38(6): 91-99. DOI： 10.11959/j.issn.1000-0801.2022089.

摘要

为了能较为全面地描述语音信号的特征信息，提高伪装检测率，提出了一种基于均匀局部二值模式纹理特征与常数Q倒谱系数声学特征相结合，并以随机森林为分类模型的伪装语音检测方法。利用均匀局部二值模式提取语音信号语谱图中的纹理特征矢量，并与常数Q倒谱系数构成联合特征，再用所获得的联合特征矢量训练随机森林分类器，从而实现了伪装语音检测。实验中，分别对其他特征参数以及支持向量机分类器模型所构建的几种伪装检测系统进行了性能对照，结果表明，所提联合特征与随机森林模型相结合的语音伪装检测系统具有最优的检测性能。

Abstract

In order to describe the characteristic information of the speech signal more comprehensively and improve the detection rate of camouflage

a spoofing speech detection method based on the combination of uniform local binary pattern texture feature and constant Q cepstrum coefficient acoustic feature was proposed

which used random forest as the classifier model.The texture feature vector in the speech signal spectrogram was extracted by using the uniform local binary mode

and the joint feature was formed with the constant Q cepstrum coefficient.Then

the obtained joint feature vector was used to train the random forest classifier

so as to realize the camouflage speech detection.In the experiment

the performances of several spoofing detection systems constructed by other feature parameters and the support vector machine classifier model were compared

and the results show that the proposed speech spoofing detection system combined with the joint feature and the random forest model has the best performance.

关键词

Keywords

references

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