采用圆周局部三值模式纹理特征的合成语音检测方法

doi:10.11959/j.issn.1000-0801.2023121

Abstract

Abstract:

In order to further improve the accuracy of synthetic speech detection, a synthetic speech detection method using texture feature based on circumferential local ternary pattern (CLTP) was proposed.The method extracted the texture information from the speech spectrogram using the CLTP and applied it as the feature representation of speech.The deep residual network was employed as the back-end classifier to determine the real or spoofing speech.The experimental results demonstrate that, on the ASVspoof 2019 dataset, the proposed method reduces the equal error rate (EER) by 54.29% and 2.15% respectively, compared with the traditional constant Q cepstral coefficient (CQCC) and linear predictive cepstral coefficient (LPCC), and reduced the EER by 17.14% compared with the local ternary pattern(LTP) texture features.The CLTP comprehensively takes into account the differences between the central and peripheral pixels in the neighborhood and between each peripheral pixel.Then it can acquire more texture information from the speech spectrogram, and improve the accuracy of synthetic speech detection.

Key words: speaker verification, synthetic speech detection, CLTP, deep residual network

CLC Number:

TP391.42

Honghui JIN, Zhihua JIAN, Man YANG, Chao WU. Synthetic speech detection method using texture feature based on circumferential local ternary pattern[J]. Telecommunications Science, 2023, 39(6): 85-95.

Figures/Tables 10

References 26

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子集	真实语音/条	欺骗语音/条	说话人/个	攻击方式
训练集	2 580	22 800	20	A01～A06
开发集	2 548	22 296	20	A01～A06
评估集	7 355	49 140	67	A07～A16

对比项	T=1	T=2	T=3	T=4
A07	5.69%	2.53%	3.94%	3.99%
A08	8.89%	6.78%	8.96%	11.66%
A09	2.40%	1.71%	2.05%	2.09%
A10	7.89%	3.07%	5.21%	5.07%
A11	5.88%	2.28%	3.71%	3.93%
A12	5.25%	3.01%	3.81%	4.17%
A13	5.56%	2.30%	5.33%	4.82%
A14	7.71%	4.13%	5.47%	5.39%
A15	6.33%	2.75%	4.80%	4.80%
A16	5.94%	3.52%	4.05%	4.43%
平均	6.25%	3.19%	4.76%	5.06%

分类器	特征	EER	精确度	灵敏度	F1分数
GMM	CQCC	9.57%	87.08%	90.43%	88.72%
	LFCC	8.51%	88.51%	91.49%	89.98%
	LTP	7.97%	94.53%	92.03%	93.26%
	CLTP	7.21%	95.06%	92.79%	93.91%
ResNet-18	CQCC	6.98%	95.23%	93.02%	94.11%
	LFCC	3.26%	97.80%	96.74%	97.27%
	LTP	3.85%	97.39%	96.15%	96.77%
	CLTP	3.19%	97.85%	96.81%	97.33%

对比项	EER	精确度	灵敏度	F1分数
A07	2.53%	98.30%	97.47%	97.88%
A08	6.78%	95.37%	93.22%	94.28%
A09	1.71%	98.85%	98.29%	98.57%
A10	3.07%	97.93%	96.93%	97.43%
A11	2.28%	98.47%	97.72%	98.09%
A12	3.01%	97.97%	96.99%	97.48%
A13	2.31%	98.44%	97.69%	98.07%
A14	4.13%	97.20%	95.87%	96.53%
A15	2.75%	98.15%	97.25%	97.70%
A16	3.52%	97.62%	96.48%	97.05%

方法	EER	t-DCF
Raw-audio+RawNet2^[25]	4.66%	0.129
FG-LCNN^[24]	4.07%	0.102
Raw-audio+Inc-TSSDNet+CABM^[26]	3.28%	0.087
本文方法	3.19%	0.085

Synthetic speech detection method using texture feature based on circumferential local ternary pattern

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[1]	Jia XU, Zhihua JIAN, Honghui JIN, Chao WU, Lin YOU, Yingxiao WU. Synthetic spoofing speech detection method based on center-symmetric local binary pattern [J]. Telecommunications Science, 2023, 39(1): 72-78.
[2]	Jian XU, Zhihua JIAN, Jiaqi YU, Yifan JIN, Lin YOU, Yunlu WANG. Completed local binary pattern based speech anti-spoofing [J]. Telecommunications Science, 2021, 37(5): 91-99.