基于联合特征与随机森林的伪装语音检测

doi:10.11959/j.issn.1000-0801.2022089

Abstract

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.

Key words: spoofing speech detection, acoustic feature, texture feature, uniform local binary pattern, random forest

CLC Number:

TP391.42

Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU. Spoofing speech detection algorithm based on joint feature and random forest[J]. Telecommunications Science, 2022, 38(6): 91-99.

Figures/Tables 8

References 27

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伪装类型	SVM		RF
伪装类型	MFCC	CQCC	MFCC	CQCC
A01	0.599	0.017	0.438	0.011
A02	0.325	0.014	0.22	0.004
A03	0.415	0.248	0.22	0.112
A04	0.414	0.153	0.326	0.018
A05	0.272	0.097	0.242	0.094
A06	0.234	0.126	0.171	0.112
A07	0.503	0.007	0.343	0.007
A08	0.359	0.024	0.253	0.033
A09	0.457	0.063	0.364	0.028
A10	0.368	0.003	0.208	0.022
A11	0.466	0.024	0.244	0.015
A12	0.428	0.093	0.375	0.015
A13	0.282	0.007	0.255	0.003
A14	0.382	0.014	0.339	0.052
A15	0.298	0.07	0.199	0.06
A16	0.417	0.037	0.244	0.018
A17	0.332	0.09	0.212	0.123
A18	0.28	0.248	0.202	0.211
A19	0.265	0.107	0.179	0.092
平均值	0.373	0.076	0.265	0.054

检测系统	平均用时/s
SVM	1 007.142
RF	0.106 7

Spoofing speech detection algorithm based on joint feature and random forest

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