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

Honghui JIN; Zhihua JIAN; Man YANG; Chao WU

doi:10.11959/j.issn.1000-0801.2023121

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

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

- Synthetic speech detection method using texture feature based on circumferential local ternary pattern
- Telecommunications Science Vol. 39, Issue 6, Pages: 85-95(2023)
- 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. 浙江省数据存储传输及应用技术研究重点实验室，浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023121
  CLC： TP391.42
- Published Online：2023-06，
  
  Published：20 June 2023
- 稿件说明：
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Honghui JIN, Zhihua JIAN, Man YANG, et al. Synthetic speech detection method using texture feature based on circumferential local ternary pattern[J]. Telecommunications science, 2023, 39(6): 85-95.
DOI：

Honghui JIN, Zhihua JIAN, Man YANG, et al. Synthetic speech detection method using texture feature based on circumferential local ternary pattern[J]. Telecommunications science, 2023, 39(6): 85-95. DOI： 10.11959/j.issn.1000-0801.2023121.

摘要

为了进一步提高合成语音检测的准确率，提出了一种采用圆周局部三值模式（CLTP）纹理特征的合成语音检测方法。该方法利用圆周局部三值模式提取语谱图中的纹理信息并作为语音的特征表示，采用深度残差网络作为后端分类器来判决语音真伪。实验结果表明，在ASVspoof 2019数据集上，与传统的常量Q倒谱系数（CQCC）和线性预测倒谱系数（LPCC）两种特征相比，该方法在等错误率（EER）上分别降低了54.29%和 2.15%，与局部三值模式（LTP）纹理特征相比，该方法在等错误率上也降低了 17.14%。圆周局部三值模式由于综合考虑了邻域内中心像素与周边像素之间以及各周边像素之间的差异，更加全面地获取了语谱图的纹理信息，提高了合成语音检测的准确率。

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.

关键词

Keywords

references

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