A method of synthetic speech spoofing detection using constant Q modulation envelope

Jia XU; Zhihua JIAN; Honghui JIN; Chao WU

doi:10.11959/j.issn.1000-0801.2023187

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A method of synthetic speech spoofing detection using constant Q modulation envelope

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

- A method of synthetic speech spoofing detection using constant Q modulation envelope
- Telecommunications Science Vol. 39, Issue 11, Pages: 107-115(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.1000-0801.2023187
  CLC： TP391.42
- Published Online：2023-11，
  
  Published：20 November 2023
- 稿件说明：
移动端阅览
Jia XU, Zhihua JIAN, Honghui JIN, et al. A method of synthetic speech spoofing detection using constant Q modulation envelope[J]. Telecommunications science, 2023, 39(11): 107-115.
DOI：

Jia XU, Zhihua JIAN, Honghui JIN, et al. A method of synthetic speech spoofing detection using constant Q modulation envelope[J]. Telecommunications science, 2023, 39(11): 107-115. DOI： 10.11959/j.issn.1000-0801.2023187.

摘要

针对传统的声学特征参数对合成语音伪装检测时存在的准确度低、未知类型合成语音检测效果较差、在噪声环境中表现欠佳的情况，提出了一种采用恒Q调制包络（constant Q modulation envelope，CQME）的合成伪装语音检测方法。该方法基于语音时域包络中包含的丰富信息，而合成语音与真实语音的包络在细节上存在较大差异，利用恒Q变换（constant Q transform，CQT）得到语音调制包络谱，并计算每个频率成分的均方根，获得CQME特征向量。再用该特征向量训练随机森林分类器，实现真伪语音的判别。实验结果表明，在ASVspoof 2019数据集上，CQME特征训练的随机森林具有较高的检测性能，对未知类型的合成语音也具有较好的检测效果。并且在多种噪声条件下，该方法仍表现出较高的检测性能，具有很好的噪声鲁棒性。

Abstract

In response to the low accuracy of synthetic speech spoofing detection based on traditional acoustic feature parameters

poor detection performance for unknown types of synthetic speech

and performance degradation in noisy environments

a method for detecting spoofing synthetic speech was proposed using constant Q modulation envelope (CQME) .The motivation of the method was from the fact that the temporal envelope of speech contained abundant information and there was a big difference in detail between the envelope of synthetic speech and genuine speech.The modulation envelope spectrum of speech was obtained by employing constant Q transform (CQT)

and the root mean square of each frequency component was calculated to derive the CQME feature vector.And then the CQME feature vector was used to train the random forest classifier for discriminating genuine speech from spoofing synthetic speech.Experimental results demonstrate that the random forest trained with CQME features achieves high detection performance on the ASVspoof 2019 dataset and exhibites good detection efficacy for unknown types of synthetic speech.Furthermore

the proposed method shows high detection performance even under various noise conditions

having excellent noise robustness.

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Keywords

references

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